# Quantifying social group evolution

## TL;DR

This paper introduces a novel clique percolation algorithm to analyze the evolution of overlapping communities in social networks, revealing how group size influences stability and adaptability over time.

## Contribution

The paper presents a new algorithm for studying community dynamics over time, providing insights into the stability and adaptability of social groups based on their size.

## Key findings

- Large groups tend to persist longer if they can change membership.
- Small groups are more stable when their composition remains fixed.
- Community lifetime can be estimated from members' time commitment.

## Abstract

The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, families, or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimisation of the society as a whole. We have developed a new algorithm based on clique percolation, that allows, for the first time, to investigate the time dependence of overlapping communities on a large scale and as such, to uncover basic relationships characterising community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist longer if they are capable of dynamically altering their membership, suggesting that an ability to change the composition results in better adaptability. The behaviour of small groups displays the opposite tendency, the condition for stability being that their composition remains unchanged. We also show that the knowledge of the time commitment of the members to a given community can be used for estimating the community's lifetime. These findings offer a new view on the fundamental differences between the dynamics of small groups and large institutions.

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Source: https://tomesphere.com/paper/0704.0744