Predicting Group Evolution in the Social Network

TL;DR

This paper introduces a new method for predicting the evolution of social groups within networks, demonstrating that simple features and classifier choice significantly impact prediction accuracy.

Contribution

A novel approach for group evolution prediction in social networks is proposed and empirically validated on multiple datasets.

Findings

Simple input features can yield high prediction accuracy

Certain classifiers outperform others in prediction tasks

Parameters of the evolution extraction method greatly affect results

Abstract

Groups - social communities are important components of entire societies, analysed by means of the social network concept. Their immanent feature is continuous evolution over time. If we know how groups in the social network has evolved we can use this information and try to predict the next step in the given group evolution. In the paper, a new aproach for group evolution prediction is presented and examined. Experimental studies on four evolving social networks revealed that (i) the prediction based on the simple input features may be very accurate, (ii) some classifiers are more precise than the others and (iii) parameters of the group evolution extracion method significantly influence the prediction quality.