A probabilistic approach to structural change prediction in evolving social networks

Krzysztof Juszczyszyn; Adam Gonczarek; Jakub M. Tomczak; Katarzyna Musial; Marcin Budka

doi:10.1109/ASONAM.2012.173

A probabilistic approach to structural change prediction in evolving social networks

Krzysztof Juszczyszyn^*, Adam Gonczarek, Jakub M. Tomczak, Katarzyna Musial, Marcin Budka

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Pages	996-1001
Number of pages	6
DOIs	https://doi.org/10.1109/ASONAM.2012.173
Publication status	Published - 1 Dec 2012
Externally published	Yes
Event	2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 - Istanbul, Turkey Duration: 26 Aug 2012 → 29 Aug 2012

Publication series

Name	Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

Conference

Conference	2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Country/Territory	Turkey
City	Istanbul
Period	26/08/12 → 29/08/12

Keywords

Mixture of markov chains
Prediction
Social networks

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10.1109/ASONAM.2012.173

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Juszczyszyn, K., Gonczarek, A., Tomczak, J. M., Musial, K., & Budka, M. (2012). A probabilistic approach to structural change prediction in evolving social networks. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 (pp. 996-1001). Article 6425629 (Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012). https://doi.org/10.1109/ASONAM.2012.173

Juszczyszyn, Krzysztof ; Gonczarek, Adam ; Tomczak, Jakub M. et al. / A probabilistic approach to structural change prediction in evolving social networks. Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012. 2012. pp. 996-1001 (Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012).

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title = "A probabilistic approach to structural change prediction in evolving social networks",

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.",

keywords = "Mixture of markov chains, Prediction, Social networks",

author = "Krzysztof Juszczyszyn and Adam Gonczarek and Tomczak, {Jakub M.} and Katarzyna Musial and Marcin Budka",

year = "2012",

month = dec,

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doi = "10.1109/ASONAM.2012.173",

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series = "Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012",

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booktitle = "Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012",

note = "2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 ; Conference date: 26-08-2012 Through 29-08-2012",

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Juszczyszyn, K, Gonczarek, A, Tomczak, JM, Musial, K & Budka, M 2012, A probabilistic approach to structural change prediction in evolving social networks. in Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012., 6425629, Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, pp. 996-1001, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, Istanbul, Turkey, 26/08/12. https://doi.org/10.1109/ASONAM.2012.173

A probabilistic approach to structural change prediction in evolving social networks. / Juszczyszyn, Krzysztof; Gonczarek, Adam; Tomczak, Jakub M. et al.
Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012. 2012. p. 996-1001 6425629 (Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Musial, Katarzyna

AU - Budka, Marcin

PY - 2012/12/1

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N2 - 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.

AB - 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.

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Juszczyszyn K, Gonczarek A, Tomczak JM, Musial K, Budka M. A probabilistic approach to structural change prediction in evolving social networks. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012. 2012. p. 996-1001. 6425629. (Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012). doi: 10.1109/ASONAM.2012.173

A probabilistic approach to structural change prediction in evolving social networks

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