Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery

TL;DR

This paper investigates how the choice of timeframe type and size affects the accuracy and outcomes of the Group Evolution Discovery (GED) method in dynamic social networks.

Contribution

It provides an extensive analysis of how different timeframe configurations influence the results of the GED method in tracking social group evolution.

Findings

Timeframe type significantly impacts GED results.

Longer timeframes may smooth out short-term changes.

Optimal timeframe selection improves group evolution detection.

Abstract

New technologies allow to store vast amount of data about users interaction. From those data the social network can be created. Additionally, because usually also time and dates of this activities are stored, the dynamic of such network can be analysed by splitting it into many timeframes representing the state of the network during specific period of time. One of the most interesting issue is group evolution over time. To track group evolution the GED method can be used. However, choice of the timeframe type and length might have great influence on the method results. Therefore, in this paper, the influence of timeframe type as well as timeframe length on the GED method results is extensively analysed.