Encyclopedia of Social Networks


Edited by: George A. Barnett

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      About the General Editor

      George A. Barnett received his B.A. and M.A. in sociology at the University of Illinois–Urbana and his Ph.D. in communication from Michigan State University. Currently, he is professor and chair of the Department of Communication at the University of California at Davis. He previously taught at Rensselaer Polytechnic Institute, State University of New York at Buffalo, the University of Texas, and the University of Maryland. Professor Barnett served as chair of the Communication and Technology Division of the International Communication Association and president of the International Network for Social Network Analysis (INSNA). He edited the Handbook of Organizational Communication, Organization–Communication: Emerging Perspectives, and Advances in Communication Sciences and has served on numerous editorial boards.

      Professor Barnett's research examines social and communication networks, how they change over time as a function of perturbations to the system in which these networks are embedded. This research has led to the development of methods and tools for the examination of social network dynamics. Substantively, he primarily studies international and intercultural networks with an emphasis on telecommunications (telephones and Internet) and their role in the process of globalization.

      His research has appeared in such journals as Global Networks, Annals of Telecommunication, American Behavioral Scientist, Social Networks, Journal of American Society of Information Science and Technology, Communication Research, Human Communication Research, and Journal of Communication.


      Humans have been forming social networks since pre-historic times. The first networks were based around the family, clan, or tribe to ensure the survival of the group. In the Book of Numbers in the Old Testament, God instructs Moses how to organize the Hebrews and thus structure the nomads' social interactions. Plato and Aristotle described the social structure of ancient Greece, and Alexander organized his armies to conquer the known world. The Church in Rome created a hierarchical structure to manage its vast networks of clergy and believers. The military and corporate world have followed these models with the goal of structuring their members' activities to coordinate their patterns of interaction to guarantee the most efficient and effective outcomes possible.

      Humans are social animals. We are members of groups. Throughout history the process of networking took place through face-to-face contact. Later we added written messages, and then electronic messaging—the telegraph, telephone, film, radio, and television. Today, we have the Internet and the various message systems that keep us in touch with one another—the process known as social networking. Our social networks are ubiquitous, determining who we are, with whom we communicate, what we think, and how we act. This encyclopedia is about social networks and their roles in history, society, our contemporary lives, and the future. I invite you to read through its pages and take a fascinating journey into the world of social networks.

      While the foundations for the study of networks (graphs) can be traced back to the Swiss mathematical Leonhard Euler (1707–83), it was not until the writings of Georg Simmel (1858–1918), a German sociologist whose studies pioneered the concept of social structure, that key precursors of social network analysis were in place. Generally, the origin of social network analysis is attributed to Jacob Moreno's development of the “sociogram” and founding of the field of sociometry in the 1930s. Sociometry is the measurement of interpersonal relations in small groups. These relations may be displayed in a sociogram, a graphic representation in which people or other social units are presented as points or circles and the relationships among the people as lines between the corresponding points. According to Stan Wasserman and Katherine Faust, this development led to two core aspects of social network analysis: the visual display of group structure and a probabilistic model of outcomes due to group structure.

      By the 1950s, numerous research groups had begun to create a field called sociometry, or social network analysis. Recognition that sociograms could be used to study social structure, especially of small groups, led to the rapid diffusion of network methods. At the same time, it was discovered that matrices (sociomatrices or adjacency matrices) could be used to represent social network data. This recognition brought the power of mathematics to the study of social structure.

      A complete history of the study of social networks is beyond the scope of this introduction. For an excellent history of social network analysis, I recommend Linton Freeman's The Development of Social Network Analysis. Also, a brief history may be found in this volume under “Network Theory.” However, let it suffice to say network models were independently discovered and/or widely adopted by anthropologists in the United Kingdom, sociologists, social psychologists, and communication scholars studying organizational processes and the diffusion of innovations (my entrée to the field), as well as information scientists, political scientists, geographers, and others. Each provided their own twist on basic concepts to satisfy their unique theoretical, methodological, and substantive needs, often working in isolation from one another.

      Meeting of Scholars

      In the mid-1970s, two meetings were held in Hawaii in an attempt to bring together the various disparate scholars interested in social networks. I was fortunate to attend one and it changed the focus of my research. These meetings culminated in the establishment of the International Network for Social Network Analysis (INSNA) and a commitment to hold annual meetings starting in 1981. These meetings became known as the Sunbelt Social Network Conference. Today, INSNA has over 700 members from over 76 countries worldwide. It publishes three journals devoted to the study of social networks: Connections (1977), founded by Barry Well-man; Social Networks (1978), launched by its first editor, Linton Freeman as well as a more recent addition, an electronic journal, the Journal of Social Structure (2000). Also linking network scholars is INSNA's listerv, SOCNET, which allows network researchers worldwide to discuss research and professional issues, make announcements, and request help from each other.

      Over the last 30 years, there has been exponential growth in computer networks from a pair of linked computers into the mother of all networks, the Internet, and the WWW, a distributed hypertext system consisting of a virtual network of contents consisting of almost 50 billion Website pages linked by over 9 billion hyperlinks. Additionally, Internet search engines such as Yahoo! and Google use network models to identify the most appropriate Websites for any given set of keyword search terms. Websites, such as Facebook, Myspace, LinkedIn, http://Classmates.com, and others have proliferated, connecting individuals with common affiliations or interests or others linked through relational ties. According to http://Alexa.com, as of June 2010, 33.3 percent of Internet users worldwide sign on to http://Facebook.com daily, an increase of over 10 percent in the preceding three months. In order to understand the complexity of this system, computer scientists and physicists have independently turned to the study of social networks.

      A Ubiquitous Study

      Today, the study of social networks is ubiquitous. It seems as if all academics are interested in networks. Natural scientists, including biologists and physicists, have changed the focus of their research from an emphasis on the attributes of individual entities to an emphasis on the relationships among the units that constitute a network or system as a whole. They have discovered the significance of connectedness, relationships, and the relative locations of the components in the larger systems. In an effort to understand the transmission of disease and the improvement in quality of life, social networks have become central to the health sciences. Everyone, including environmental scientists and organizational managers, has realized that by examining natural and human networks, we can more efficiently work together to accomplish organizational and societal goals. As Tom Valente has recently written,

      Suddenly, network analysis was being discussed in science publications, periodicals, and the popular press with surprising frequency. New companies were created to store and use social networks, and social networking was a hot new pastime. At the same time, network analysis began to become accepted as a legitimate and necessary tool to answer research questions posed in many fields.

      A social network is generally defined as a system composed of a set of social actors, individually called nodes, and a collection of social relations, called links or ties, which specify how these actors are relationally tied. Network analysis is a form of systems analysis. It serves two purposes: revealing the underlying social structures and discovering the dynamic interactions among social actors. Network analysis identifies the system's structure by examining the relations among the system components, its actors.

      Computer and information systems are electronic communication networks that are structured in order that data, information, and messages may be passed from one location in the network to another over multiple links—transmission line—and through various nodes (generally computers). When these networks link people or higher-level social systems, such as formal organizations or nation-states, they become social networks, or, more precisely, computer-mediated social infrastructures such as those represented by social networking Websites.

      A New Model

      Network analysis represents a new model of scientific research. It differs from traditional research by focusing on the relations among the systems components, generally individual actors, rather than the attributes of the particular components. Rather than predicting the behavior of the individual based upon these attributes, network analysis predicts the one's future behavior based on its relationships with the other actors and its position relative to the others in the social system. As such, network analysis is a structural theory. For example, while traditional management theory would predict organizational turnover based on an individual's psychological (motivation, commitment, or ambition) and demographic (age, gender, or education) characteristics, network analysis focuses on the actor's relations to the other actors in the organization. Who does the individual talk to about work and socially related topics? Who are his/her friends? Have any of these individuals left the organization recently?

      Recent research has shown that the network indicators are much more powerful predictors than the psychological or sociological variables. This applies not only to small groups and organizations but also to society at large. One's social network not only determines whom he/she votes for but even if they smoke and their weight. It is not just the behavior of individual people that may be considered a function of their position in their social networks; the behavior of nations, such as does a country go to war and with whom, may be predicted by the country's position in a network composed of the international community of nations.

      This Encyclopedia of Social Networks provides a guide to understanding social networks, including both those populated exclusively by humans and those constructed by people for the purpose of linking individuals into larger social systems. These later networks make possible work groups, formal organizations, and nations, as well as computer systems from which certain properties emerge that have become the defining characteristics of the Information Age, where the production and distribution of information represents global society's primary economic activity. Indeed, the Internet, a computer-mediated social network, represents the infrastructure of the Information Age. It has accelerated globalization, the process of linking people and institutions from distant localities transcending physical distance and national boundaries such that events in one corner of the world have an impact in another. This has led to the creation of a global community, or what Marshall McLuhan labeled the “global village.”

      The study of social networks is an interdisciplinary endeavor. To understand the ethical, legal, technical, and social issues involved in application to various scientific, engineering, and social challenges, scholars from all over the world have contributed to this compendium. In it, readers will find accessible descriptions of some of the key technical aspects of network science as well as its history and application in a wide variety of social contexts.

      Discussion and Inquiry

      This encyclopedia is not a technical primer for the scientific study of social networks. There are numerous introductory texts on the topic. Two detailed and comprehensive volumes that may serve as reference guides are Stan Wasserman and Katherine Faust's (1994) Social Network Analysis: Methods and Applications and Matthew Jackson's (2008) Social and Economic Networks. This title is intended to establish social networks as a topic of discussion and inquiry. While it does contain technical definitions and brief descriptions of the scientific research on social networks and social networking, the majority of the volume is to inform the reader about the history of social networks in a variety of contexts. Its goal is to help us understand the role social networks play in our lives and how networks may impact social policy and the world in the future.

      The Encyclopedia of Social Networks is not an absolutely complete and comprehensive source of knowledge. The expectations that a single title could be both encyclopedic in knowledge and based on a rapidly emerging field are impossible to meet. Network science as an academic area of study is far too diverse, dynamic and contentious. Indeed, even the identity of network science is up for grabs. Is it part of mathematics, an application of graph theory or statistics? Is it simply a systems methodology, or an interdisciplinary social science? Many of its aspects are still largely unexamined.

      These two volumes contain more than 400 entries describing social networks and social networking. Even the selection of the encyclopedia's title was a topic for debate. Its authors come from a plethora of academic disciplines—mathematics and statistics, computer and information science, biology, all the social sciences, as well as law and the humanities. Each author brought his/her unique perspective and understanding of what constitutes a social network to their entry. However, it does represent an extensive, if not totally comprehensive, expert and informed basic reference for the study of social networks. It is meant to facilitate inquiry for students and others interested in but unfamiliar with the science of social networks and the process of social networking. It is also likely to prove valuable to the contributors because the field is still so diffuse and the scholarship still developing that academics will find useful starting points on topics outside their particular expertise.

      Finally, the encyclopedia may even serve as a point of entry into the network of social network analysts. Contacting an author, either directly or through his/her research publications, may lead to contacts with other network scholars and their research, thus creating a new personal network for the reader or their establishment as a node in the larger community of network scholars.

      George A.Barnett General Editor

      Reader's Guide

      List of Articles

      List of Contributors

      • Natalia Abuín Vences, Madrid Complutense University
      • Robert Ackland, Australian National University
      • Jason Michael Adams, Hawaii Pacific University
      • Ana Adi, University of the West of Scotland
      • Filip Agneessens, University of Groningen
      • Shah Jamal Alam, University of Michigan
      • Matthew Allen, Curtin University of Technology
      • Inês Amaral, Institute Superior Miguel Torga
      • Katherine (Katie) Anderson, Rutgers, the State University of New Jersey
      • Terri Anderson, University of California, Los Angeles
      • Rina Arya, University of Wolverhamptom
      • Joshua Azriel, Kennesaw State University
      • Shonell Bacon, Texas Tech University
      • Andrea J. Baker, Ohio University at Lancaster
      • Stephanie Alice Baker, Indian Institute of Technology, Bombay
      • Stijn Bannier, Vrije Universiteit Brussel
      • George Barnett, University of California, Davis
      • John Barnhill, Independent Scholar
      • Brenda Battleson, State University of New York at Buffalo
      • Magdalena Bielenia-Grajewska, University of Gdansk
      • Stephanie Bjork, Paradise Valley Community College
      • Örjan Bodin, Stockholm Resilience Centre
      • Robert Bodle, College of Mount St. Joseph
      • danah boyd, Microsoft Research New England
      • Sharon Brennan, University of Melbourne
      • Breno Bringel, Complutense University of Madrid
      • Taina Bucher, University of Oslo
      • Kelli Burns, University of South Florida
      • Vincent Buskens, Utrecht University
      • Peter Buzzi, A Sense of Self
      • Jason Vincent Cabañes, University of Leeds
      • Deborah Cai, Temple University
      • Julie Cajigas, Cleveland State University
      • Emma Campbell, Australian National University
      • Kathleen Carley, Carnegie Mellon University
      • Pilar Carrera, Universidad Carlos III de Madrid
      • Peter Carrington, University of Waterloo, Ontario
      • Stacy Carter, Texas Tech University
      • Mark Chignell, University of Toronto
      • Alvin Chin, Nokia Research Center
      • Marjee Chmiel, National Geographic, The JASON Project
      • Elizabeth Christian, Louisiana Tech University
      • Vincent Chua, University of Toronto
      • Elaine Coburn, American University of Paris
      • Jordi Comas, Bucknell University
      • Amanda Harmon Cooley, South Texas College of Law
      • Paula Cordeiro, Technical University of Lisbon
      • Rense Corten, Utrecht University
      • Russ Crawford, Ohio Northern University
      • Charles Crothers, Auckland University of Technology
      • Kenneth Culton, Niagara University
      • Alan Daly, University of California, San Diego
      • James A. Danowski, University of Illinois at Chicago
      • Gareth Davey, Hong Kong Shue Yan University
      • Daniëlle De Vooght, Vrije Universiteit Brussel
      • Jana Diesner, Carnegie Mellon University
      • Leslie Dinauer, University of Maryland, University College
      • Brenda Donelan, Northern State University
      • Patrick Doreian, University of Pittsburgh
      • Aziz Douai, University of Ontario
      • Marten Duering, Kulturwissenschaftliches Institut NRW
      • Jaroslav Dvorak, Vytautas Magnus University
      • Adam Earnheardt, Youngstown State University
      • Samuel Ebersole, Colorado State University, Pueblo
      • Anthony Edwards, Tarleton State University
      • Jennifer Edwards, Tarleton State University
      • el-Sayed el-Aswad, United Arab Emirates University
      • Meredith Eliassen, San Francisco State University
      • Lauri Elliott, Independent Scholar
      • Nicole Ellison, Michigan State University
      • Sothy Eng, California Center for Population Research
      • Melike Erdogan, Dokuz Eylul University
      • Paul Falzone, Green Mountain College
      • Jane Fedorowicz, Bentley University
      • Efim Fidrya, North-Eastern State University, Russia
      • Edward Fink, University of Maryland
      • Wes Fondren, Coastal Carolina University
      • Ethan Fosse, Harvard University
      • Terrill Frantz, Peking University
      • Thomas Friemel, University of Zurich
      • Markus Gamper, Universtiät Trier
      • Alberto García, Universidad Complutense de Madrid
      • Carolyn Garrity, Louisiana State University
      • Robert Gehl, University of Utah
      • Armando Geller, George Mason University
      • Nazanin Ghanavizi, University of Sydney
      • Frédéric Godart, INSEAD
      • Amanda K. Goddard, Western Michigan University
      • Daniel Gonshorek, Knox College
      • Michael Goodchild, University of California, Santa Barbara
      • Melanie Grellhesl, Texas Tech University
      • Francesca Grippa, University of Salento
      • Justin Gross, University of North Carolina at Chapel Hill
      • Wojciech Gryc, University of Oxford
      • Germaine Halegoua, University of Wisconsin, Madison
      • Jason Hannan, Northwestern University
      • Derek Hansen, University of Maryland
      • David Hanson, State University of New York, at Potsdam
      • Jeff Heer, Stanford University
      • Colin Helb, Elizabethtown College
      • Jason Helfer, Knox College
      • DeAndre Henderson, Knox College
      • Rachel Hildebrandt, Independent Scholar
      • Brian Hirshman, Carnegie Mellon University
      • Julie Hite, Brigham Young University
      • Bernie Hogan, Oxford Internet Institute
      • Jun Huang, Columbia University
      • Dina A. Ibrahim, San Francisco State University
      • Yasmin Ibrahim, Queen Mary University of London
      • Muhammad Zubair Iqbal, University of Ulster, Coleraine Campus
      • Thomas Jacobson, Temple University
      • Sung Jun Jo, Utica College
      • Jan-Erik Johanson, University of Helsinki
      • David Jones, Old Dominion University
      • Jennifer Jones, University of the West of Scotland
      • Shannon Kahle, Penn State University
      • Naim Kapucu, University of Central Florida
      • Theodoras Katerinakis, Drexel University
      • John Kelly, Morningside Analytics
      • Patrick Kenis, Tilburg University
      • Justin Kirkland, University of North Carolina at Chapel Hill
      • Lorraine Kisselburgh, Purdue University
      • Juraj Kittler, St. Lawrence University
      • James Kitts, Columbia University
      • David Knoke, University of Minnesota
      • Tobias Kohler, Jacobs University, Bremen
      • Emmanuel Koku, Drexel University
      • Piotr Konieczny, University of Pittsburgh
      • Tetiana Kostiuchenko, National University of Kyiv–Mohyla Academy
      • Till Krause, Justus Liebig University Giessen
      • Bill Kte'pi, Independent Scholar
      • Jordan K. Lanfair, Knox College
      • Gediminas Lankauskas, University of Regina
      • MooSung Lee, Hong Kong Institute of Education
      • Linda Leung, University of Technology, Sydney
      • Loet Leydesdorff, University of Amsterdam
      • Corey J. Liberman, Marymount Manhattan College
      • Eric Liguori, Louisiana State University
      • Jonathan Lillie, Loyola University, Maryland
      • Lissette Linares, University of Texas at Austin
      • Simon Lindgren, Umea University
      • Christopher Little, University of Toronto
      • Kim Lorber, Ramapo College of New Jersey
      • Laszlo Lorincz, Corvinus University of Budapest
      • Miranda Lubbers, Universitat Autònoma de Barcelona
      • Andras Lukacs, Loyola University Chicago
      • Michael Macy, Cornell University
      • Dalhia Mani, University of Minnesota
      • Peter Marsden, Harvard University
      • Joseph Maslen, University of Manchester
      • Evan Massey, Knox College
      • Christopher McCarty, University of Florida
      • Heather McIntosh, Boston College
      • Mitch McKenney, Kent State University
      • Jill McTavish, The University of Western Ontario
      • Claudia Megele, A Sense Of Self
      • Robert Meier, University of Nebraska at Omaha
      • Gabriele Melischek, Austrian Academy of Sciences
      • Sharon Meraz, University of Illinois, Chicago
      • Ines Mergel, Syracuse University
      • Andy Miah, University of the West of Scotland
      • Sara Miller, Indiana University–Purdue University Indianapolis
      • Momoyo Mitsuno, Surugadai University
      • Artur Mkrtchyan, Yerevan State University
      • José Luis Molina, Universitat Autònoma de Barcelona
      • Peter Monge, University of Southern California
      • Nienke Moolenaar, Twente University
      • Aldo de Moor, Independent Scholar
      • Jessica Moore, North Carolina State University
      • Mel Moore, University of Northern Colorado
      • Gabriel Moreno Esparza, Universidad de Guadalajara
      • Eric Morgan, University of South Florida
      • Christine Moser, VU University Amsterdam
      • Heather Lea Moulaison, University of Ottawa
      • Christopher Mugimu, Makerere University
      • Tobias Müller-Prothmann, Pumacy Technologies AG
      • Josh Murray, State University of New York, Stony Brook
      • Seyed Mussavi Rizi, George Mason University
      • Kaustubh Nande, Ohio University
      • Jennifer Watling Neal, Michigan State University
      • Zachary Neal, Michigan State University
      • Balint Neray, Corvinus University of Budapest
      • Gillian Newton, Rutgers University
      • Konrad Ng, University of Hawaii at Manoa
      • Carl Nordlund, Central European University, Budapest
      • Andrew Ó Baoill, Cazenovia College
      • Francesca Odella, University of Trento
      • Christian Oggolder, Austrian Academy of Sciences
      • Kristie Ogilvie, California State University at San Bernardino
      • Amalya Oliver, Hebrew University, Jerusalem
      • Trevor Owens, Library of Congress
      • Judit Pal, Corvinus University of Budapest
      • Han Woo Park, Yeungnam University
      • Adam Kristian Peckman, University of Sydney
      • Erika Polson, University of Denver
      • Liza Potts, Old Dominion University
      • Christina Prell, University of Sheffield
      • Luca Prono, Independent Scholar
      • Keith Provan, University of Arizona
      • Narissra Maria Punyanunt-Carter, Texas Tech University
      • Anabel Quan-Haase, University of Western Ontario
      • Anthony Quinn, Dublin Institute of Technology
      • Raluca Radu, University of Bucharest
      • Steven Rafferty, University of Southern California
      • Faaiza Rashid, Harvard Business School
      • Katie M. Reno, Western Michigan University
      • Michael Restivo, State University of New York, Stony Brook
      • Leslie Reynard, Washburn University
      • Scott Richmond, Western Michigan University
      • Jessica Rivait, Michigan State University
      • Isaias Rivera, Tecnologico de Monterrey (ITESM)
      • Seyed M. M. Rizi, George Mason University
      • Garry Robins, University of Melbourne
      • Jack Rollins, Indiana University, Bloomington
      • Vincenzo Romania, University of Padova
      • Bojana Romic, University of Arts, Belgrade
      • Devan Rosen, University of Hawaii at Manoa
      • Steffen Roth, Université de Genève
      • Stephanie M. Ruhl, Ohio University
      • Susana Salgado, New University of Lisbon
      • Maureen Savage, Western Michigan University
      • Stephen Schroth, Knox College
      • Josef Seethaler, Austrian Academy of Sciences
      • Raquel Segura, Universidad Complutense de Madrid
      • Marc-David Seidel, University of British Columbia
      • Jan Servaes, University of Massachusetts at Amherst
      • Jaffer Sheyholislami, Carleton University
      • Mirit D. Shoham, Ohio University
      • Michelle Shumate, University of Illinois at Urbana-Champaign
      • Paul Skalski, Cleveland State University
      • Marc Smith, Connected Action Consulting Group
      • Ashima Sood, Independent Scholar
      • Stephen Spates, Western Michigan University
      • Patric Spence, Western Michigan University
      • Jesse St. Charles, Carnegie Mellon University
      • Martin Stark, University of Trier
      • Mafalda Stasi, Coventry University
      • Carmen Stavrositu, University of Colorado at Colorado Springs
      • H. Cecilia Suhr, Bloomsburg University
      • Doug Tewksbury, Niagara University
      • David Tindall, University of British Columbia
      • Emanuela Todeva, University of Surrey
      • Arthur Peter Tomasino, Bentley University
      • Magnus Torfason, Columbia University
      • Davide Torsello, University of Bergamo, Italy
      • Marcella Bush Trevino, Barry University
      • Kent Truett, Texas Tech University
      • Didem Türkoglu, Bogaziçi University
      • Thomas Valente, University of Southern California
      • Arnout van de Rijt, State University of New York, Stony Brook
      • Aram Vartikyan, Yerevan State University
      • Jessica Vitak, Michigan State University
      • Regina von Görtz, German Research Institute for Public Administration
      • Don Waisanen, Baruch College, City University of New York
      • Katherine Walker, Virginia Commonwealth University
      • Joseph Walzer, University of Wisconsin, Milwaukee
      • Jennifer Ware, North Carolina State University
      • Richard Waters, North Carolina State University
      • Matthew Weber, Duke University
      • Adele Weiner, Metropolitan College of New York
      • Barry Wellman, University of Toronto
      • Robert Whitbred, Cleveland State University
      • Julie Wiest, High Point University
      • Jeff Williams, National University of Córdoba
      • Meredith Worthen, University of Oklahoma
      • Mychailo Wynnyckyj, National University of Kyiv–Mohyla Academy
      • Anna Zamora, Columbia University
      • Amira Zebidi, Texas Tech University
      • Theodoras Zervas, North Park University Chicago
      • Radwan Ziadeh, New York University
      • Lorna Lueker Zukas, National University

      Chronology of Social Networks

      c. 20,000 B.C.E.: Homo sapiens emerged in Africa around 200,000 years ago. The first networks were based around the family, clan, or tribe, and sought to ensure the survival of the group.

      c. 10,000 B.C.E.: The Neolithic Revolution introduced agriculture, which offered a more stable food supply, and so populations began to grow. The necessity to remain in one place while the crops grew, and the necessity to store harvests, resulted in semipermanent dwellings and over time gave rise to the first cities that established urban civilization as the norm for a large majority of humanity.

      c. 7500 B.C.E.: Catal Hayek, located in Anatolia (modern Turkey), the first agricultural town, brought together nearly 6,000 people. In theses early towns, residents networked to make the land more productive, to celebrate the harvest, and to ensure success in the future. The first social networks formed around the concept of agriculture. The presence of trade items at sites like these indicated that commercial ties were sources of networking.

      c. 5400 B.C.E.: In order to make the inhospitable environment of Mesopotamia (Modern Iraq) fertile, large numbers of people were needed to work on constructing irrigation projects. This brought together larger numbers of people around with common interests and led to the formation of the first city-states such as Eridu, Uruk, and Ur. Life in cities opened up new possibilities for social networking, allowing for specialization of economic tasks and the rise of social classes.

      c. 3000 B.C.E.: The written word, originating with cuneiform, became, and remains, one of the most powerful tools for maintaining social networks across space. To keep track of the growing trade between city-states, the marks that merchants made on clay tablets to keep track of inventory evolved into the cuneiform writing system. Egyptian hieroglyphics also came into use around this time, possibly influenced by Mesopotamian cuneiform.

      c. 2300 B.C.E.: Sargon of Akkad created the first multinational empire, which became a basis for the creation of new networks. The empire was a primary means of political organization history until the middle of the 20th century and has provided the chance for diverse peoples to interact and network around common interests. The most stable empires over time—the Chinese, the Japanese, the Persian, the Roman, and the British Empires, for instance—have offered conquered peoples some stake in the direction of events in their locality, which has provided network opportunities for many.

      c. 1800 B.C.E.: Abraham, the patriarch (although in differing views) of the Jewish, Christian, and Muslim religions, which are three of the great global social networks, entered into a covenant with Yahweh, the Hebrew deity.

      c. 1700 B.C.E.: The Hindu religion, centered on the Rigveda, began in India, adding to the social networks based on religion.

      1200 B.C.E.: The Chinese developed their writing system to aid social networking during the Shang dynasty (1766–1122 B.C.E.). The ideograms that became the characters of the language were discovered on oracle bones, which were turtle bones used to divine the future, and that also indicates the formation of social networks following a common religion.

      c. 900 B.C.E.: The Zhou dynasty (1045–256 B.C.E.) in China began the first postal service, delivering mail between government offices.

      c. 800 B.C.E.: In Greece, the Archaic Period saw the creation of the polis, or city-state, which included some provision for participatory governance, the foundation for democracy, and one of the great social networks based on politics.

      776 B.C.E.: The Olympic Games brought together Greeks to compete in feats of strength, speed, and other athletic pursuits. This served as a social networking opportunity for the various poleis that made up Magna Graecia, or Greater Greece. Along with the Oracle at Delphi, the Olympic Games unified Greek city-states from Sparta to Miletus (in modern Turkey) around a shared culture. This was also the first recorded use of homing pigeons to send messages; in this case, the winning results of the games were sent to Athens by pigeons.

      c. 563 B.C.E.: Siddhartha Gautama, the founder of Buddhism, was born.

      480 B.C.E.: The seeds of Western civilization were sown. When the Persian Empire invaded Greece in revenge for the Ionian Revolt (499–493 B.C.E.), which was led by the Ionian League, with the help of Athens. Eretria Herodotus, the first historian, wrote that the initial clash between Greek forces at Thermopylae saw not only a conflict between armies but also a battle between the free Greeks and Eastern despotism. This narrative would create the idea of Western Civilization, one of the most pervasive social networks. Today, one can find individuals who follow the Western lifestyle in all corners of the world, which makes this a powerful force to bring people together around a common heritage, whether this heritage is homegrown or whether it has been imposed by colonialism or cultural imperialism.

      334 B.C.E.: Hellenic culture spreads. The Greek poleis were successful in defeating the Persian invasions of their homeland, but handled victory badly. In the Peloponnesian War (431–404 B.C.E.), which pitted the Delian League led by Athens against the Peloponnesian League led by Sparta, they accomplished what the Persians could not. This allowed a more vigorous power, the Macedonians under Philip and Alexander, to take up the cause of Hellenic culture. The Macedonians under Alexander the Great (356–323 B.C.E.) spread the Hellenic cultural networks through Persia and as far as India.

      c. 320 B.C.E.: Aristotle wrote his work Politics, in which he argued that “man is a political animal,” which can also be construed as meaning that humans are social animals. The proclivity of humans to form social networks therefore is due to our intrinsic nature.

      27 B.C.E.: The Roman Empire blossomed. Growing from a small republic (509–27 B.C.E.) to a universalizing empire (27 B.C.E.–476 C.E.), the Romans spread Greco-Roman culture from Britain across Europe and north Africa to Asia. Rome became a cultural vacuum that pulled in religious and social traditions from all parts of the empire. For over 500 years, Greco-Roman culture, along with the cultures of subjected peoples, flowed back and forth across the empire and created opportunities for scholars, soldiers, merchants, and others to create social networks centered on their particular interests.

      c. 5 B.C.E.: Jesus of Nazareth, the founder of Christianity, was born in Judea, then part of the Roman Empire. Later executed by crucifixion, his followers and later believers spread the new religion to Rome, where followers faced persecution before the faith became the official religion of the empire, and one of the great social networks in 380 C.E.

      105 C.E.: Zailun, a court official of the Han dynasty, was credited with making the first sheet of paper. Archaeologists argue that the actual invention of paper took place some 200 years earlier, but as a medium for messaging, paper was superior to the papyrus of the Egyptians (c. 4000 B.C.E.) or parchment made from animal skins that, which was common in the rest of the world. From China, papermaking would spread to Korea and Japan, and finally to the West though the Islamic world.

      c. 305: The Chinese invented the first printing press, which used carved wooden blocks to make ink impressions on paper.

      476: In the 5th century C.E., peoples whom the Romans called “barbarians,” took advantage of the growing weakness of the empire to sweep through its borders and destroy it. Though the Roman Empire fell, the idea of Rome continued to be attractive. From Charlemagne, who created (with papal help) the forerunner to the Holy Roman Empire in 800 C.E. to the European Economic Union formed by the Treaty of Maastricht in 1993, the idea of uniting Europe under the control of one body has been powerful.

      c. 530: The Catholic Church was one of the few institutions to survive the fall of Rome and constituted a source of stability in a troubled time. As it took on more civic functions, some believed that the Church had become too worldly, and many dropped out of society. Following the adoption of the rules of Saint Benedict, monasteries became important engines of commerce during the Middle Ages. The Benedictine rules charged monks to engage in useful labor as well as to perform their religious devotions.

      610: Muhammad, a merchant living in the Arabian city of Mecca, said he received revelations from God. He later began preaching these revelations and gathering followers into his network, which became another of the great social networks: Islam.

      1090: Hassan-i-Sabbah captured the fortress at Alamut, which would be used as a base for the Hashashim, who assassinated their enemies rather than fight as armies. The Hashashim, who gave us the word assassin, reportedly used opiates to ready themselves for attacks and attacked both Christians and Muslims.

      1095: The Crusades (1095–1272), which marked the European attempt in the name of Christendom to wrest control of the Holy Land from the Seljuk Turks, and the Muslim retaliation gave rise to social networks such as the Knights Templar (1119–1314) and the Knights Hospitaller (1099–present).

      c. 1350: Humanism, the focus on human agency rather than religion, was reintroduced into Europe during the Renaissance (14th–16th centuries). Humanism created social networks that would challenge the religiously centered networks predominant during the Middle Ages.

      c. 1390: The first known mention of the Freemasonry, a fraternal organization with members worldwide. At various times, Freemasons were seen as conspirators bent on world domination and even gave rise to the Anti-Masonic Party in the United States from 1828 to 1832, which gave the American political system the political convention.

      1455: Johannes Gutenberg invented the printing press in the West, which allowed for the rapid dissemination of ideas throughout Euurope.

      c. 1500: The Iroquois League, a confederation of Native-American tribes, was formed in the northeastern region of the present-day United States.

      1517: Martin Luther, a German priest, wrote his 95 Theses, which disputed Catholic Church theology and charged it with corruption. The Church, already weakened by controversies concerning the papacy, reacted strongly, and much of Europe descended into heavily armed social networks during the Thirty Years War. This continued to constrain the makeup of an individual's potential social networks for centuries.

      1635: The Tokugawa Shogunate enacted legislation to codify the sankin kotai, or hostage system, which required nobles to live half the year in Edo (modern Tokyo), which led to the establishment of social networks centered on the ukiyo, or “floating world,” or brothel district. This culture saw the creation of the first fan clubs centered on famous Kabuki actors, sumo wrestlers, and geishas, whose images were printed as posters using the ukiyo-e process of woodblock printing.

      1660: The Royal Society for the Improvement of Natural Knowledge was founded in England. This exploration was not limited to the scientific elite, however, and many ordinary people began corresponding with each other, forming social networks to push back the frontiers of human understanding of the natural world.

      1677: Baruch Spinoza, echoing Aristotle, argued that the definition of man as a social animal was generally held to be true.

      1765: In response to the British government's imposition of the Stamp Act, the Sons of Liberty formed to lead the resistance to policies they saw as threatening to their liberty in the American colonies. Their actions, along with the governmental philosophies of the Enlightenment, would lead to the American Revolution (1776–81) and the formation of the United States.

      1776: Adam Weishaupt formed the Illuminati, a secret organization, in Bavaria. Conspiracy theorists argue that the organization still lives and that it controls worldwide events.

      1789: The Jacobins was formed, a radical group that came to be the leading network of the French Revolution. This network, under the leadership of Maximilien Robespierre, later led the effort to eradicate all opponents to the revolution through the Reign of Terror (1793–94).

      1811: The Luddites, inspired by Ned Ludd, a worker who smashed machines in a fit of rage, began to destroy the new textile factories of the Industrial Revolution. Luddites were reacting to the new working conditions brought on by the rise of the factory that led to loss of autonomy for workers.

      c. 1830: Steam-powered locomotives and boats began a transportation revolution. The possibility of belonging to social networks was expanded by the virtual reduction in the size of the world. Soon, trains took fans to watch prizefights, steamboat races drew crowds, and tourism became a feature of life.

      1833: William Lloyd Garrison and Arthur Tappan organized the American Anti-Slavery Society, which worked for the abolition of the institution of slavery in the United States.

      1835: Alexis de Tocqueville published Democracy in America, in which he wrote about American's penchant for forming associations. The citizens of the new nation formed networks to address any matter, large or small, that caught their attention or concern. His description of the civil society of the new nation addressed the explosion of social networks that accompanied its development after independence.

      1835: Samuel Morse invented the Morse Code, which made possible the sending of messages by telegraph possible and facilitated long-distance contact between members of social networks.

      1844: George Williams, while working in London, formed the Young Men's Christian Athletic Association (YMCA), which sought to create places where young men could escape the temptations of the city. The organization, aided by philosophy of Muscular Christianity, spread globally from there. In the United States, YMCA officials invented several new games, including versions of basketball, volleyball, and handball.

      1845: Alexander Cartwright formed the New York Knickerbockers, the first official baseball team. Their first recorded game, played between two teams, took place at the Elysian Fields in Hoboken, New Jersey, in 1846. From that start, and aided by the American Civil War (1861–65), baseball spread across the nation, becoming the “national pastime” and a social network that would bring together people from the Americas to Asia.

      1848: Karl Marx and Friedrich Engels published the Communist Manifesto, which provides a political alternative to monarchy, democracy, and the republic. The book divides the world into the bourgeoisie and the proletariat, or the factory owners or managers and the workers.

      1850: The Taiping Heavenly Kingdom formed in the eastern provinces of China. Led by Hong Xiuquan, who believed he was the brother of Jesus Christ, the Taipings held large parts of China until defeated by Western and Qing dynasty (1644–1912) forces in 1864.

      1865: The Ku Klux Klan formed in 1865 by veterans of the Confederate Army to resist Reconstruction (1865–77) in the southern United States. The Klan used intimidation and outright violence to persecute newly freed blacks and to prevent them from exercising their political and social rights.

      1866: The Satsuma-Choshu Alliance formed in Japan to resist the Tokugawa Shogunate. The two domains also sought to learn from the West, after initial attempts to turn back European and American ships ended disastrously. With the successful overthrow of the shogun and restoration of direct imperial rule during the Meiji Restoration (1868), several samurai from these domains dominated the Japanese government until the early decades of the 20th century and enabled Japan to catch up to the West.

      1869: The first intercollegiate football game pitted Rutgers University against Princeton.

      1876: Alexander Graham Bell patented the telephone, a new communication tool that would facilitate social networking.

      1877: Thomas Edison patented the phonograph, which allowed listeners to enjoy recorded music. Numerous and diverse social networks would form around favorite singers, bands, and musical genres.

      1901: Guglielmo Marconi sent the first wireless transatlantic radio signal from England to Newfoundland.

      1908: Robert Baden-Powell published Scouting for Boys, which led to the formation of the Boy and Girl Scouts organizations worldwide. Inspired by what Baden-Powell considered the British soldier's lack of wilderness skill in the Boer War (1899–1902), scout troops became an international feature, including social networks that ranged from building paramilitary skills to those focused on public service.

      1910: Thomas Edison demonstrated the first motion picture with sound. The movie became a medium for the growth of social networks.

      1912: The African National Congress formed in South Africa to resist the domination of the black African population by Africans of European descent.

      1917: The Bolshevik Revolution in Russia gave communism a geographic base and led to the Cold War (1946–91) following World War II (1939–45). This divided much of the world into two opposing social networks, the communist and the capitalist.

      1919: The League of Nations (1919–46), created by the Treaty of Versailles, which ended World War I, sought to avoid a repeat of the slaughter of what was called the “war to end all wars.” Without enforcement powers, and lacking the membership of the United States, the League was little more than a social network.

      1921: Adolf Hitler took control of the National Socialist Party in Germany. Hitler eventually became chancellor of Germany and began taking steps that led to World War II.

      1934: Jacob Moreno publishes Who Shall Survive?, which led to the development of the study of social networks.

      1949: The first network television began in the United States. Hit television series such as I Love Lucy or Seinfeld would form the basis for social networks centered on fandom.

      1954: The National Liberation Front (FLN) was created in Algeria to obtain independence from France. Groups such as the FLN, the Vietminh in Vietnam, and the Indian National Congress worked to gain independence for their nations from the colonial powers. Decolonization occurred primarily between 1945 and 1999 and led to an increase in world nations from 50 to 192.

      1957: Martin Luther King, Jr., Bayard Rustin, and others formed the Southern Christian Leadership Conference (SCLC), which became a leading organization to fight for civil rights for African Americans.

      1958: Gerald Holtom designed the logo for the Campaign for Nuclear Disarmament, which was created at the University of Glasgow, Scotland, in 1957. The ubiquitous “peace sign” became the visible symbol for the youth movement that swept the world in the 1960s.

      1962: Sociologist Everett Rogers published his influential text titled Diffusion of Innovations. The book examines how novelties seem to disperse throughout, and become adopted by those in, societies. In so doing, Rogers argues that innovations are more likely to diffuse throughout society if the “right” people become the innovators and then spread the idea to others: the early adopters, the early majority, the late majority, and the laggards.

      1969: ARPANET, the first computer-to-computer network was created, paving the way for the Internet.

      1970: Sociologists Everett Rogers and Dilip Bhowmik explained the idea that social actors do not always benefit from having similar people (in terms of demographics, attitudes, and behaviors) as part of their social networks: a term that is widely known as homophily. Instead, Rogers and Bhowmik introduce the idea that having “optimal heterophily,” most aptly defined as a network of social actors that are demographically, attitudinally, and behaviorally different, can provide many network advantages for those considered a part of it.

      1971: Sociologists Francois Lorrain and Harrison White forwarded the idea of structural equivalence in social networks, which is the idea that, when two social actors in a given social network have ties to the same individuals, they then have the same importance and significance in the social network.

      1971: Communication scholar Bill Richards developed the first version of NEGOPY, a computer program that allowed one to engage in social network analysis, determining how members of a given social network were (or were not) socially connected to one another. In addition to statistically determining the ties between and among social actors, NEGOPY also allowed users to discover network cliques, network liaisons, and network isolates.

      1973: Sociologist Mark Granovetter distinguished two types of network ties (strong versus weak) in an effort to distinguish relationships based on time, emotional intensity, intimacy, and reciprocity. His “strength of weak ties” argument claims that relationships that are not very long-standing, not very emotionally intense, not very intimate, and not very reciprocal can, in fact, be beneficial for social actors, inasmuch as they provide certain (often limited) resources.

      1979: Sociologist Linton Freeman clarified three of the centrality measures still used to determine the importance of one's position in a given social network. According to Freeman, one is important in a given network to the extent that he has degree centrality (having more ties in a given social network compared to other social actors), betweenness centrality (having the opportunity to tie disconnected social actors to each other), and/or closeness centrality (being socially proximate to other social actors in a network).

      1985: Sociologist Pierre Bourdieu popularized the term social capital, which refers to the limited resources that one is provided by the social actors to which he/she is connected. Social capital is yet another way of describing one's importance in a given social network, based on the inherent value of one's network ties.

      1986: Sociologists Henri Tajfel and John Turner developed Social Identity Theory, which claims that social actors come to identify themselves based on the networks in which they are embedded. This theory has helped explain attitude formation and behavioral engagement as they relate to the study of social networks.

      1992: Sociologist Ron Burt created the term structural hole to indicate both the problems and opportunities associated with a lack of a tie between and among actors in a social network. He argued that it is problematic because the network is lacking a tie and, therefore, is not closed. A structural hole provides an opportunity, however, because a social actor can fill this structural hole and, as result, can gain importance in the social network.

      1993:http://Match.com became the first Web-based dating service.

      1995: Robert Putnam published Bowling Alone: America's Declining Social Capital, in which he described what he saw as a decline in membership in face-to-face social networking. He cited the declining membership in civic organizations, as well as league bowling, to sound an alarm that American democracy might be in danger.

      1995:http://Classmates.com, a Website devoted to bringing together former classmates in the United States, appeared on the Internet.

      1997:http://SixDegrees.com, a Web-based networking site, was launched. This site sought to bring together people with indirect social connections.

      2002: Friendster, a social networking Website that allows users to view profiles of others before making contact, debuted. Friendster is particularly popular across the Asian continent.

      2004: Facebook launched its Web-based social networking site.

      2006: MySpace joined the social networking world.

      2008: Both Barack Obama and John McCain, contenders for the presidency of the United States, launch profiles on MySpace and other social networking sites.

      2009: Forty-five percent of employers, according to a CareerBuilder survey, reported they used social networking sites to check job candidates.

      2011: Social networking Websites, such as Facebook, played a pivotal role in organizing protesters during the demonstrations against auhoritarian rule in the Middle East and north Africa.

      RussCrawford, Ohio Northern University
    • Glossary

      • Actual network: The sum total of social beings in a given network based not only on self-report but also on confirmation from those individuals named by ego.
      • Adjacency: The extent to which there is a direct tie, without the introduction of an intermediary source, between two social actors.
      • Adjacency matrix: A square matrix, consisting of rows and columns, which represents the presence or absence of ties between and among all actors in a given social network.
      • All channel network: A type of social network whereby all social actors in the network have a direct tie or link with all others regardless of position or hierarchy.
      • Alter: A sociological reference to any given member of an ego's social network.
      • Attribute: A particular characteristic of a social actor that makes this individual part of a given social network
      • Betweenness centrality: A measure of a property of an actor's position in a network whereby one benefits by being in a structural position that allows him/her to connect previously disconnected social actors together.
      • Binary data: A statistical measure, in the form of a 1 or a 0, indicating either the presence or the absence of a relationship with another social actor in a given social network.
      • Boundary: Any characteristic of a given social network that creates criteria for both inclusion and exclusion in the network.
      • Boundary spanner: An individual who is part of one social network but, because of his/her connection to another social network, connects individuals from both networks together.
      • Bounded network: A particular social network that is somehow isolated from, and disconnected from, other existing social networks.
      • Bridge: An individual in a given social network who connects two previously disconnected social actors or groups together and is part of both of these social networks.
      • Brokerage: The act of a social actor in a given social network somehow connecting otherwise disconnected social actors together.
      • Centrality: One's level of importance or significance in a given social network as determined by the actor's ties to the other actors in the network.
      • Circle network: A type of social network whereby every social actor in a given social network can only communicate with the individuals located to the right and left of them.
      • Clique: A group of social actors in a given social network who share common interests, behaviors, or characteristics and who are all somehow connected to all others.
      • Closeness centrality: A measure of a property of an actor's position in the network whereby a social actor in a given social network can reach all others in the least number of steps, as compared to all others in the network.
      • Closure: When, in a given social network, all social actors are somehow connected to all others.
      • Clustering: When the great majority of (if not all) social actors within a given social network have a direct or indirect relationship with all others within a portion of the network.
      • Cognitive theory: The idea that all of those part of a given social network will begin to engage in uniform thinking.
      • Community of practice: The idea that those part of a given social network share common interests, attitudes, thoughts, ideas, and/or behaviors.
      • Complete network: The systematic investigation of all social actors part and parcel of a given social network.
      • Confirmatory tie: When attempting to study one's social network, an individual named by ego as being part of his/her network agreeing that he/she is, in fact, a social alter.
      • Constraint: When communication, or other social behaviors, must flow between and among social actors but cannot because of a lack of a network tie.
      • Contagion theory: The idea that all of those part of a given social network will begin to engage in uniform behavior due to communication among the actors.
      • Contrived network: A social network of which one is a part without much choice or flexibility.
      • Coorientation: An example of symmetry whereby two actors in a given network not only have a social tie to one another but also begin to think in uniform ways.
      • Core: A structure in a given social network whereby certain social actors have relationships with each other, become more central in the network, and thereby form a tight-knit subgroup.
      • Cross-sectional study: An empirical method of data collection whereby a given social network is studied in one (often random) moment in time.
      • Cycle: A path of relationships among social actors in a social network that begins and ends at the same point.
      • Density: A statistical measure of the number of actual network relationships between and among social actors as compared to the number of possible network relationships among the social actors.
      • Dependency theory: The idea that two social actors within a given network will rely on each other for certain (often limited) resources.
      • Diffusion: The notion that products, ideas, and information spread throughout a given population quicker if those with these products, ideas, and information are well networked in society.
      • Directedness: The reciprocity, or lack thereof, of relationships between and among social actors in a given social network.
      • Direction: Which way communication flows between two social actors in a given network.
      • Disconfirmatory tie: When attempting to study one's social network, an individual named by ego as being part of his/her network disagrees that he or she is, in fact, a social alter.
      • Distance: The total number of relational steps between and among any social actors in a given social network.
      • Diversity: The idea that social actors in a given social network are dissimilar regarding interests, attitudes, behaviors, and/or abilities.
      • Dunbar's number: The idea that the cognitive upper limit of the number of individuals with whom ego can create and maintain stable relationships is approximately 150.
      • Dyad: A communication relationship between two social actors in a social network, or simply any pair of actors.
      • Edge: Any type of relationship between two nodes in a given social network.
      • Efficiency: The ease with which any social actor in a given social network can reach all other social actors.
      • Ego: A sociological reference to oneself when referring to one's given social network.
      • Egocentric network: A social network comprised of an individual and anyone else with whom this individual has a direct or indirect connection.
      • Eigenvector centrality: A particular measure of centrality based not merely on how many ties one has to all others but rather the extent to which one is socially connected to others who, themselves, are central.
      • Embeddedness: The extent to which individual social actors in a given social network are connected to all others, impacting their ideas, attitudes, and behaviors.
      • Emergent network: A social network that is not prescribed but rather one that materializes based on certain needs, wants, attitudes, desires, and commonalities.
      • Faction: When all social actors in a given social network not only have relationships with all others in that social network but also do not have relationships with any others outside that social network.
      • Frequency: A quantitative assessment of the amount of time, or number of times, that two or more social actors in a given social network communicate with one another.
      • Gatekeeper: A social actor who controls the flow of communication between any part(s) of the social network.
      • Geodesic: According to social network and graph theories, the shortest possible distance between any two points or nodes in a network.
      • Graph: A pictorial representation of the relationships (or lack thereof) between and among social actors in a given network.
      • Heterophily: When the social actors considered part of a given social network do not share certain attitudes, characteristics, and/or behaviors.
      • Homophily: When the social actors considered part of a given social network all share certain attitudes, characteristics, and/or behaviors.
      • In-degree centrality: A statistical measure of the number of individuals within a given social network who claim to have a relationship with oneself.
      • Indirect link: A relationship between two individuals that is mediated by a third social actor.
      • Integration: The proportion of an actor's social links that are, themselves, linked to each other.
      • Intensity: The overall level of strength of the relationships between and among social actors in a given social network.
      • Isolate: An individual within a given social network who has no relationships with all other social actors.
      • Knowledge management: The ability of social actors in a given social network to not only know where information exists but also to be able to codify it and share it with others.
      • Liaison: An individual in a given social network who connects two previously disconnected social actors together but is not part of either of these social networks.
      • Line network: A type of social network wherein social actors can only communicate with other social actors if they have hierarchical power over them.
      • Longitudinal study: An empirical method of data collection whereby a given social network is studied at multiple (often nonrandom) moments in time.
      • Multimodal structures: The idea that social actors are embedded in networks, which are themselves embedded in networks, which are themselves embedded in networks.
      • Multiplex tie: Two individuals share a relationship with one another based on more than one overarching characteristic or category.
      • Name generator technique: A method for obtaining network data by asking ego to name all those with whom he/she communicates for particular reasons.
      • Name interpreter technique: A method for studying social networks whereby an individual indicates not only whether he/she communicates with others but also why communication occurs and what relationship exists.
      • Network mapping: Creating a graphical representation of the relationships between and among social actors in a given social network.
      • Node: Any individual member of a given social network.
      • Opinion leader: A social actor within a given social network who, because of his/her position, has the ability to socially influence other network members, based on topic-specific and/or subject-specific ideas.
      • Orientation: In a given social network, one will hold similar attitudes and engage in similar behaviors to the extent that his/her network alter would think or do the same.
      • Out-degree centrality: A statistical measure of the number of individuals within a given social network with whom one claims to have a relationship.
      • Path: A linear model of the trajectory of edges between and among social actors in a given social network.
      • Perceived network: All of the social beings within one's network based merely on self-report, not by confirming with those individuals named by ego.
      • Periphery: A structure in a given social network whereby certain social actors have relationships with others in the core subgroup but do not have relationships with noncore social actors.
      • Population: Each and every individual member of a given social network.
      • Position generator technique: A method of studying social networks whereby one's structural position in a given network is determined, studying such variables as access to others, the presence of social alters, and access to resources.
      • Positional network: A map, or pictorial representation, of how two or more individuals in a given social network are socially connected to one another.
      • Power: An index of the amount of dependence that certain social actors in a given social network have on others.
      • Prestige: A measure of one's importance or significance in a given social network as determined by the number of links one receives.
      • Prominence: Any given social actor's overall importance and/or power in a given social network.
      • Range: Within a given network, the extent to which ego communicates with others who are dissimilar to him/her and/or has access to different resources.
      • Reachability: The ability of all social actors within a given social network to somehow connect to all others.
      • Reciprocity: The extent to which individual A claims to have a relationship with individual B and individual B claims to have a relationship with individual A.
      • Regular equivalence: When two social actors in a given social network have ties not to the same individuals but to individuals who are socially similar and, thus, have the same importance and significance in the social network.
      • Relational network: A map, or pictorial representation, of why two or more individuals in a given social network are socially connected to one another.
      • Roster technique: A method of obtaining network data by asking ego to look at the names of all of those part of his/her network and then indicate whether he/she communicates with them.
      • Sample: A certain proportion of individual members in a given social network.
      • Size: The number of social actors in a social network.
      • Small world network: A collection of several small, tightly connected groups of social actors who have few ties to each other.
      • Social capital: A measure of importance or significance based not only on the number of relationships that one has in society or the number of social networks of which one is a part but also on the reasons that one has the types of relationships that he/she does.
      • Social influence: The idea that those part of one's social network can influence ego because of network similarity.
      • Social network analysis: The systematic study of the ways in which social actors in a given population are socially connected to one another and the results of these connections.
      • Sociogram: A pictorial representation of a given social network where relationships between and among individuals can be seen.
      • Socio-matrix: A mathematical array of numbers that depicts whether or not there exists a relationship between two individuals in a given social network.
      • Sparse network: A social network in which all individuals are not socially connected to all others, either directly or through a mediator, or generally a network with relatively few links.
      • Stability: The extent to which two social actors in a given network, or the entire network, continue their relationship over time.
      • Star: A social actor in a given social network who is considered highly central, while the other actors are connected only though the central actor.
      • Status: One's index of importance in a social network.
      • Strong tie: A relationship with another individual where the amount of time invested, the emotional intensity, and the amount of intimacy are all relatively high.
      • Structural equivalence: When two social actors in a given social network have ties to the same individuals and, thus, have the same importance and significance in the social network.
      • Structural hole: When, in a given social network, not all social actors are somehow connected to all others, which is generally filled by a liaison.
      • Subgroup: A subset of social actors drawn from a larger social network.
      • Symmetry: The extent to which communication between any two social actors in a given social network is bidirectional.
      • Theory of mutual self-interest: The idea that if one benefits from his/her position within his/her social network, then all social actors within that social network will similarly benefit.
      • Theory of self-interest: The idea that one will be able to benefit merely because of his/her position within his/her social network.
      • Threshold: The idea that a social actor in a given network will begin to engage in a particular behavior based on the number of individuals in that social network who already engage in the behavior.
      • Tie: A network connection that links any two individuals in a given social network together.
      • Transitivity: In a given triadic network, if individual A claims to have a relationship with individual B, and individual B claims to have a relationship with individual C, then individual A will, by definition, also have a relationship with individual C.
      • Triad: A communication relationship among three social actors in a given social network.
      • Uniplex tie: Two individuals share a relationship with one another based on one overarching characteristic or category.
      • Valued data: A method of data collection whereby ego is not only asked whether or not he/she has a network tie with others but also how strong or weak the relationship is.
      • Weak tie: A relationship with another individual where the amount of time invested, the emotional intensity, and the amount of intimacy are all relatively low.
      • Wheel network: A type of social network whereby one central actor has access to all other social actors, and all other social actors have access to the one central actor, but all other social actors do not have access to each other.
      Corey J.Liberman, Marymount Manhattan College

      Resource Guide

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      • American Journal of Sociology
      • American Sociological Review
      • Behavioral Science
      • Communication Research
      • Connections
      • Human Communication Research
      • Human Organization
      • International Journal of Electronic Commerce
      • Journal of Communication
      • Journal of Computer-Mediated Communication
      • Journal of Early Adolescence
      • Journal of Mathematical Sociology
      • Journal of Quantitative Criminology
      • Journal of School Psychology
      • Journal of Social Networking and Mining
      • Journal of Social Structure
      • Office Technology and People
      • Psychometrika
      • Social Forces
      • Social Networks
      • Sociometry

      Photo Credits

      Volume 1: Architect of the U.S. Capitol: 218; Christian Oggolder: 335, 336; Emanuela Todeva: 97; iStockphoto: 3, 75, 82, 106, 198, 133, 243, 257, 285, 351, 375, 380, 439, 454, 475; Kevin Ross: 492; Los Alamos National Laboratory: 506; Library of Congress: 17, 29, 89, 116, 127, 147, 215, 292, 358, 365, 411 left and right, 467; http://Photos.com: 250; Marc Smith: 246, 248; Mike Shadle: 397; http://Morguefile.com: 101, 121, 203, 185, 392; NASA: 432; National Archives: 140, 370; National Institutes of Health: 46; National Park Service: 53, 58; National Oceanic and Atmospheric Administration: 96, 326, 234; Rico Shen: 307; Sage Ross: 166; Sandia National Laboratories: 229; USAID: 10, 43, 65, 111, 192, 319, 340, 344, 402, 462; U.S. Agricultural Research Service: 416; U.S. Census: 275; U.S. Central Intelligence Agency: 499; U.S. Department of Defense: 159, 222, 387, 480; U.S. Department of Energy: 178; U.S. Department of the Interior: 485; U.S. Department of State: 333, 427; U.S. Fish and Wildlife Service: 266; U.S. Geological Survey: 68; U.S. Occupational Safety and Health Administration: 171; White House: 150; Wikimedia Commons: 36, 210, 299, 312, 444, 451.

      Volume 2: Ali Mansuri: 756; City of Port Washington, WI: 908; David Drexler: 918; http://Elev8.com: 801; Ethan Fosse: 887 top; middle, bottom. Facebook: 839, 900; Federal Emergency Management Agency: 726; Frédérick Godart: 825; iStockphoto: 515, 527, 534, 558, 603, 610, 624, 705, 730, 738, 782, 794, 820, 827, 884, 924, 931; James A. Kitts: 873; Kentucky Legislature Research Commission: 650; Kevin Rosseel: 587; Lost Man Project: 669; Library of Congress: 568, 594, 674, 834 left and right, 851, 889, 893, 957; http://Morguefile.com: 638, 743, 844, 863; NASA: 763; National Library of Medicine: 617 top and bottom; National Oceanic and Atmospheric Administration: 575; New York City Council/William Alatriste: 582, 913; Richard D. Waters: 722; Sandia National Laboratories: 645; Ursula Murray Husted: 539; USAID: 520, 551, 657, 681, 692, 770, 775, 818, 877, 964, 979; U.S. Air Force: 664; U.S. Army: 808; U.S. Immigration and Customs Enforcement: 688; U.S. Department of Defense: 565, 700, 710, 719, 750, 815, 856, 905, 936, 973; U.S. Department of Homeland Security: 546; U.S. Fish and Wildlife Service: 950; U.S. Marine Corps: 789, 905; U.S. Transportation Security Administration: 631; Wikimedia Commons: 943.

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