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Entropy 2015, 17(5), 3053-3096; doi:10.3390/e17053053

Predicting Community Evolution in Social Networks

1
Department of Computational Intelligence, Wrocław University of Technology, Wyb.Wyspiańskiego 27, 50-370 Wrocław, Poland
2
AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: J. A. Tenreiro Machado
Received: 28 February 2015 / Revised: 4 May 2015 / Accepted: 5 May 2015 / Published: 11 May 2015
(This article belongs to the Section Complexity)

Abstract

Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods. View Full-Text
Keywords: social network; social network analysis (SNA); social community; social group detection; group evolution; group evolution prediction; group dynamics; classifier; feature selection; GED; SGCI social network; social network analysis (SNA); social community; social group detection; group evolution; group evolution prediction; group dynamics; classifier; feature selection; GED; SGCI
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Saganowski, S.; Gliwa, B.; Bródka, P.; Zygmunt, A.; Kazienko, P.; Koźlak, J. Predicting Community Evolution in Social Networks. Entropy 2015, 17, 3053-3096.

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