A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks

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 evo...

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Bibliographic Details
Main Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J. M., Musial, K., Budka, M.
Format: Conference Proceeding
Language:English
Subjects:
Complex networks
Educational institutions
Electronic mail
Markov processes
mixture of Markov chains
prediction
Servers
Social network services
social networks
Trajectory
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Summary: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.
DOI:10.1109/ASONAM.2012.173