Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Strategies for the Diffusion of Innovations on Social Networks
Computational Economics
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining and Visualizing the Evolution of Subgroups in Social Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
A visual-analytic toolkit for dynamic interaction graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Monitoring Network Evolution using MDL
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Modeling the flow and change of information on the web
Proceedings of the 21st international conference companion on World Wide Web
Statistical Analysis and Data Mining
Dynamic communities and their detection
Acta Cybernetica
Multi-scale dynamics in a massive online social network
Proceedings of the 2012 ACM conference on Internet measurement conference
A significance-driven framework for characterizing and finding evolving patterns of news networks
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Evolutionary Community Detection for Observing Covert Political Elite Cliques
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Incremental local community identification in dynamic social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 17th International Database Engineering & Applications Symposium
Analysis of dynamic resource access patterns in a blended learning course
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
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Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these graphs from a static point of view. The study of the evolution of these graphs over time can provide tremendous insight on the behavior of entities, communities and the flow of information among them. In this work, we present an event-based characterization of critical behavioral patterns for temporally varying interaction graphs. We use nonoverlapping snapshots of interaction graphs and develop a framework for capturing and identifying interesting events from them. We use these events to characterize complex behavioral patterns of individuals and communities over time. We show how semantic information can be incorporated to reason about community-behavior events. We also demonstrate the application of behavioral patterns for the purposes of modeling evolution, link prediction and influence maximization. Finally, we present a diffusion model for evolving networks, based on our framework.