A cluster algorithm for graphs
A cluster algorithm for graphs
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
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
MONIC: modeling and monitoring cluster transitions
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
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A visual-analytic toolkit for dynamic interaction graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Characterizing and predicting community members from evolutionary and heterogeneous networks
Proceedings of the 17th ACM conference on Information and knowledge management
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Change analysis in spatial datasets by interestingness comparison
SIGSPATIAL Special
Change Analysis in Spatial Data by Combining Contouring Algorithms with Supervised Density Functions
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A viewpoint-based approach for interaction graph analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On evolutionary spectral clustering
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
iOLAP: A framework for analyzing the internet, social networks, and other networked data
IEEE Transactions on Multimedia - Special section on communities and media computing
A particle-and-density based evolutionary clustering method for dynamic networks
Proceedings of the VLDB Endowment
CHRONICLE: A Two-Stage Density-Based Clustering Algorithm for Dynamic Networks
DS '09 Proceedings of the 12th International Conference on Discovery Science
Analyzing change in spatial data by utilizing polygon models
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Outcome aware ranking in interaction networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding spread blockers in dynamic networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Transient crowd discovery on the real-time social web
Proceedings of the fourth ACM international conference on Web search and data mining
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
Intrinsically dynamic network communities
Computer Networks: The International Journal of Computer and Telecommunications Networking
Community detection in Social Media
Data Mining and Knowledge Discovery
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
A framework for summarizing and analyzing twitter feeds
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting aggregate social activities using continuous-time stochastic process
Proceedings of the 21st ACM international conference on Information and knowledge management
Modeling dynamic behavior in large evolving graphs
Proceedings of the sixth ACM international conference on Web search and data mining
Identification of Group Changes in Blogosphere
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Identifying Long Lived Social Communities Using Structural Properties
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Group and link analysis of multi-relational scientific social networks
Journal of Systems and Software
Community structure and evolution analysis of OSN interactions around real-world social phenomena
Proceedings of the 17th Panhellenic Conference on Informatics
Understanding evolving group structures in time-varying networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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 non-overlapping 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 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.