Analysis of group evolution prediction in complex networks

TL;DR

This paper introduces a flexible, multi-stage method for predicting the evolution of groups in complex social networks, validated through extensive empirical studies on real-world data.

Contribution

It presents a novel, adaptable framework for group evolution prediction that incorporates new features and analyzes various parameters affecting prediction accuracy.

Findings

The method effectively predicts group evolution in diverse networks.

New predictive features improve accuracy of evolution forecasts.

External data enrichment enhances prediction models.

Abstract

In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic and mutli-stage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well.