Predicting Community Evolution in Social Networks

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...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2015-05, Vol.17 (5), p.3053-3096
Main Authors: Saganowski, Stanisław, Gliwa, Bogdan, Bródka, Piotr, Zygmunt, Anna, Kazienko, Przemysław, Koźlak, Jarosław
Format: Article
Language:English
Subjects:
Classification
classifier
Communities
Digital media
Evolution
Feature extraction
feature selection
GED
group dynamics
group evolution
group evolution prediction
Mathematical models
Polishes
SGCI
social community
social group detection
social network
social network analysis (SNA)
Social networks
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e17053053