Exploratory Social Network Analysis with Pajek

Abstract

This was the first textbook on social network analysis integrating theory, applications, and professional software for performing network analysis (Pajek). Step by step, the book introduces the main structural concepts and their applications in social research with exercises to test the understanding. An application section explaining how to perform the network analyses with Pajek software follows each theoretical section. Pajek software and datasets for all examples are freely available, so the reader can learn network analysis by doing it. In addition, each chapter offers case studies for practising network analysis. In the end, the reader has the knowledge, skills, and tools to apply social network analysis in all social sciences, ranging from anthropology and sociology to business administration and history.

Analysis of virtual communities supporting OSS projects using social network analysis

“…The strength of a tie indicates how strong the relationship is [49]. The main goal of social network analysis is detecting and interpreting patterns of social ties among actors [31]. In the case of OSS projects, several actors can be distinguished.…”

“…Out-degree concept is based on the idea of centrality of a person within a community, but understanding centrality in the sense of a minimum distance to the rest of the members of the community. A second approach to centrality rests on the idea that a person is more central if he or she is more important as an intermediary in the communication network [31]. This approach is based on the concept of betweenness.…”

“…The betweenness centrality of a vertex is the proportion of all geodesics between pairs of other vertices that include this vertex and betweenness centralization of the network is the variation in the betweenness centrality of vertices divided by the maximum variation in betweenness centrality scores possible in a network of the same size. [31]. The betweenness centrality of the network is also detailed in Table 1 for each year.…”

Exploring the “Middle Earth” of network spectra via a Gaussian matrix function

“…[80]; Drugs: Social network of injecting drug users (IDUs) that have shared a needle in the last six months [82]; Zachary: Social network of friendship between members of the Zachary karate club [83]; College: Social network among college students in a course about leadership. The students choose which three members they wanted to have in a committee [84]; ColoSpring: The risk network of persons with HIV infection during its early epidemic phase in Colorado Spring, USA, using analysis of community wide HIV/AIDS contact tracing records (sexual and injecting drugs partners) from 1985-1999 [85]; Galesburg: Friendship ties among 31 physicians [64]; High_Tech: Friendship ties among the employees in a small high-tech computer firm which sells, installs, and maintain computer systems [64,86]; Saw Mills: Social communication network within a sawmill, where employees were asked to indicate the frequency with which they discussed work matters with each of their colleagues [64,87];…”

Community Structure, Individual Participation and the Social Construction of Merit

“…So, we also computed "proximity prestige" [8]. This indicator is equal to the number of vertex that a given vertex can reach according to the arcs present in the network divided by the mean length of path to reach all these vertices.…”