Structural analysis of hypertexts: identifying hierarchies and useful metrics
ACM Transactions on Information Systems (TOIS)
Friendster and publicly articulated social networking
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Social Bookmarking in the Enterprise
Queue - Social Computing
Mining Social Networks for Targeted Advertising
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Visualizing an enterprise social network from email
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Local Topology of Social Network Based on Motif Analysis
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Temporal Changes in Connection Patterns of an Email-Based Social Network
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
E-mail networks and leadership performance
Journal of the American Society for Information Science and Technology
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Different ways of detecting structural changes in email-based social networks are presented in the paper. A social network chosen for experiments was created on the basis of the Wroclaw University of Technology email server logs covering the period of 20 months. Structural parameters like degree centrality and prestige, clustering coefficients as well as betweenness and closeness centrality were computed for each of the consecutive months and their changes were analyzed. Our aim was to make an insight into dynamics of Internet-based social networks based on email service. It was found that the major changes in the structure of the network concern its local topology. Global indices like betweenness and closeness centrality remain relatively stable which also concerns the distribution of the local parameters such as degree centrality and prestige. However, the network size and local topology changes significantly which may be detected with motif analysis and visible changes in node clustering coefficients.