Computational organization theory
Computational organization theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Introduction to Algorithms
Modeling Relationships among Multiple Graphical Structures
Computational & Mathematical Organization Theory
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Reverse engineering an agent-based hidden Markov model for complex social systems
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Extraction, characterization and utility of prototypical communication groups in the blogosphere
ACM Transactions on Information Systems (TOIS)
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We present a machine learning approach to discovering the agent dynamics or micro-laws that drives the evolution of the social groups in a community. We set up the problem by introducing a parameterized probabilistic model for the agent dynamics: the acts of an agent are determined by micro-laws with unknown parameters. Our approach is to identify the appropriate micro-laws which corresponds to identifying the appropriate parameters in the model. To solve the problem we develop heuristic expectation-maximization style algorithms for determining the micro-laws of a community based on either the observed social group evolution, or observed set of communications between actors. We present the results of extensive experiments on simulated data as well as some results on real communities, e.g., newsgroups.