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Mining and Visualizing the Evolution of Subgroups in Social Networks

This research paper proposes statistical methods and visualization techniques to analyze the formation and timely change of subgroups in online communities. It presents two approaches: statistical analyses and visualization for relatively stable communities, and detection of subgroup evolution in highly fluctuating communities. The data set used is taken from an online international student community, with over 1000 members from 50 countries and 250,000 guestbook entries over 18 months.

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Mining and Visualizing the Evolution of Subgroups in Social Networks

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  1. Mining and Visualizing the Evolution of Subgroups in Social Networks Falkowsky, T., Bartelheimer, J. & Spiliopoulou, M. (2006) IEEE/WIC/ACM International Conference on Web Intelligence, pp. 52-58 Presented by Danielle Lee

  2. Outline • Problem • Research Purpose • Data Set • First Approach : Statistical Analyses and Visualization for relatively stable communities • Second Approach : Detection of the subgroup evolution in high fluctuating communities

  3. Problem • A community has rather stable structure with a small amount of fluctuating members and they participate in over a long time. • Another community has high dynamic structure whose members and their networks keep changing over time. • Different community detection and visualization methods are needed.

  4. Research Purpose • To propose statistical method and visualization to analyze the formation of subgroups and the timely change of online communities on the level of sub-groups

  5. Data Set • Taken from an online international student community in the University of Magdeburg. • About 1000 members from more than 50 countries • 250,000 guestbook entries over a period of 18 months

  6. Evolution of Subgroups in Static Structure (Contd.) • Mining for subgroups in Social Networks • Partitioning data by time axis • Weight graph Gt of interactions between individuals for each time windows is built. • Hierarchical edge betweenness clustering of the graph is applied in each time window

  7. Evolution of Subgroups in Static Structure (Contd.) Sub- groups Communication within one community Detailed information at a certain time point time

  8. Evolution of Subgroups in Static Structure (Contd.) • Analyzing Subgroup Dynamics • Track a detected subgroup over time by measuring the structural equivalence • Stability • Density and cohesion • Euclidean distance • Correlation coefficient • Group activity • The measures are computed for each time window • Fixed : A chosen time window is compared with all other windows • Periodical : Each time window is compared to the previous time window

  9. Evolution of Subgroups in Static Structure Kinds of Measure-ment Each Subgroup

  10. Dynamics of Communities with Fluctuating Members (contd.) • Clustering subgroups as a community • Establish a graph of subgroups to denote similarity about them • Similarity have been discovered as the overlap of members between two subgroups • Two subgroups are similar if their overlap exceeds a given threshold.

  11. Dynamics of Communities with Fluctuating Members (contd.) • Visualizing the Evolution of Subgroups Community Clustering Control Panel

  12. Dynamics of Communities with Fluctuating Members (contd.) • Community History View

  13. Dynamics of Communities with Fluctuating Members

  14. Thank you

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