Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Computational organization theory
Computational organization theory
Randomized algorithms
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
MML Markov classification of sequential data
Statistics and Computing
Destabilization of covert networks
Computational & Mathematical Organization Theory
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Finding tribes: identifying close-knit individuals from employment patterns
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
Constant-factor approximation algorithms for identifying dynamic communities
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Mining online shopping patterns and communities
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Finding hidden group structure in a stream of communications
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Modeling and multiway analysis of chatroom tensors
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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A communication network is a collection of social groups that communicate via an underlying communication medium (for example newsgroups over the Internet). In such a network, a hidden group may try to camoflauge its communications amongst the typical communications of the network. We study the task of detecting such hidden groups given only the history of the communications for the entire communication network.We develop a probabilistic approach using a Hidden Markov model of the communication network. Our approach does not require the use of any semantic information regarding the communications. We present the general probabilistic model, and show the results of applying this framework to a simplified society. For 50 time steps of communication data, we can obtain greater than 90% accuracy in detecting both whether or not their is a hidden group, and who the hidden group members are.