An algorithm for drawing general undirected graphs
Information Processing Letters
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Structural and temporal analysis of the blogosphere through community factorization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Users in Volatile Communities: Studying Active Participation and Community Evolution
UM '07 Proceedings of the 11th international conference on User Modeling
Characterizing and predicting community members from evolutionary and heterogeneous networks
Proceedings of the 17th ACM conference on Information and knowledge management
An event-based framework for characterizing the evolutionary behavior of interaction graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Detecting Changes over Time in a Knowledge Sharing Community
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
CHRONICLE: A Two-Stage Density-Based Clustering Algorithm for Dynamic Networks
DS '09 Proceedings of the 12th International Conference on Discovery Science
Making sense of meaning: leveraging social processes to understand media semantics
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Extraction, characterization and utility of prototypical communication groups in the blogosphere
ACM Transactions on Information Systems (TOIS)
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Groups without tears: mining social topologies from email
Proceedings of the 16th international conference on Intelligent user interfaces
Tracking communities in dynamic social networks
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Community Discovery via Metagraph Factorization
ACM Transactions on Knowledge Discovery from Data (TKDD)
A spectral analysis approach for social media community detection
SocInfo'11 Proceedings of the Third international conference on Social informatics
Bipartite graphs for monitoring clusters transitions
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Visualizing the evolution of community structures in dynamic social networks
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Real Time Distributed Community Structure Detection in Dynamic Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A Framework for the Forensic Analysis of User Interaction with Social Media
International Journal of Digital Crime and Forensics
Summarizing dynamic Social Tagging Systems
Expert Systems with Applications: An International Journal
Adaptive evolutionary clustering
Data Mining and Knowledge Discovery
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
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A social network consists of people who interact in some way such as members of online communities sharing information via the WWW. To learn more about how to facilitate community building e.g. in organizations, it is important to analyze the interaction behavior of their members over time. So far, many tools have been provided that allow for the analysis of static networks and some for the temporal analysis of networks - however only on the vertex and edge level. In this paper we propose two approaches to analyze the evolution of two different types of online communities on the level of subgroups: The first method consists of statistical analyses and visualizations that allow for an interactive analysis of subgroup evolutions in communities that exhibit a rather membership structure. The second method is designed for the detection of communities in an environment with highly fluctuating members. For both methods, we discuss results of experiments with real data from an online student community.