Probabilistic Approach to Structural Change Prediction in Evolving Social Networks.

Juszczyszyn, K., Gonczarek, A., Tomczak, J., Musial, K. and Budka, M., 2012. Probabilistic Approach to Structural Change Prediction in Evolving Social Networks. In: International Workshop on Complex Social Network Analysis (CSNA 2012) co-located with International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), 26-29 August 2012, Kadir Has University, Istanbul, Turkey.

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Abstract

We propose a predictive model of structural changes in elementary subgraphs 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 subgraph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:social networks; mixture of Markov chains; prediction
Subjects:UNSPECIFIED
Group:School of Design, Engineering & Computing
ID Code:20437
Deposited By:Unnamed user with email symplectic@symplectic
Deposited On:11 Sep 2012 15:58
Last Modified:11 Sep 2012 15:58

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