Predicting Community Evolution in Social Networks

TL;DR

This paper presents methods for predicting social community evolution in social networks using historical data, feature extraction, and classification models, demonstrating improved accuracy with longer evolution chains across multiple datasets.

Contribution

Introduces a framework combining SGCI and GED methods with feature-based classification for community evolution prediction, validated on real social media datasets.

Findings

Longer evolution chains improve prediction accuracy.

GED-based method performs best with chains of length 3 to 7.

SGCI achieves optimal prediction with chains of 3 to 5 periods.

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 to 5 last periods.