Social networks and online environments: when science and practice co-evolve

• Published in: Social network analysis and mining Vol. 1; no. 1; pp. 27 - 42
• Main Authors: Rosen, Devan, Barnett, George A., Kim, Jang Hyun
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01-01-2011; Springer Nature B.V
• Subjects: Animation; Applications of Graph Theory and Complex Networks; Bandwidths; Cluster analysis; Communication; Communications networks; Computer mediated communication; Computer Science; Data; Data Mining and Knowledge Discovery; Digital media; Economics; Game Theory; Geography; Humanities; Information systems; Information technology; Internet; Law; Mass media; Methodology of the Social Sciences; Network analysis; Original Article; Social and Behav. Sciences; Social media; Social network analysis; Social networks; Social structure; Statistics for Social Sciences; Structural analysis; Virtual environments; Visualization; Singapore; United Kingdom > UK; Uruguay; United States > US; Japan; India; Canada; China; Brazil; Israel; Australia; Germany; Indonesia; Social network analysis; Hyperlink networks; Social media; Computer-mediated communication; Social networking; Information systems
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-010-0011-7

The science of social network analysis has co-evolved with the development of online environments and computer-mediated communication. Unique and precise data available from computer and information systems have allowed network scientists to explore novel social phenomena and develop new methods. Additionally, advances in the structural analysis and visualization of computer-mediated social networks have informed developers and shaped the design of social media tools. This article reviews some examples of research that highlight the ways that social network analysis has evolved with online data. Examples include the international hyperlink network, political blogs and hyperlinks, social media, and multi-user virtual environments. The data available from online environments makes several important contributions to network science, including reliable network flow data, unique forms of relational data across a myriad of contexts, and dynamic data allowing for longitudinal analysis and the animation of social networks.