Connectivity and inference problems for temporal networks
Journal of Computer and System Sciences - Special issue on STOC 2000
ACM SIGKDD Explorations Newsletter
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
The convergence of social and technological networks
Communications of the ACM - Remembering Jim Gray
Mining Periodic Behavior in Dynamic Social Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
The Time-Series Link Prediction Problem with Applications in Communication Surveillance
INFORMS Journal on Computing
Adaptive Networks: Theory, Models and Applications
Adaptive Networks: Theory, Models and Applications
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We propose a predictive model of structural changes in elementary sub graphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network analyzed in short, one day-long time windows, and reveals distinctive patterns of evolution of connections on the level of local network topology. We argue that the network investigated in such short timescales is highly dynamic and therefore immune to classic methods of link prediction and structural analysis, and show that in the case of complex networks, the dynamic sub graph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server.