Fast community structure local uncovering by independent vertex-centred process

TL;DR

This paper introduces a decentralized local method for community detection in networks, where vertices broadcast degree-based info and an external process uncovers community structures, validated on artificial and real data.

Contribution

It proposes a novel decentralized approach for community detection using local vertex information and an external process, improving scalability and decentralization.

Findings

Effective on artificial data

Validated on real-world networks

Shows scalability and accuracy

Abstract

This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours. A decentralised external process then unveils the community structure. The relevance of the proposed method is experimentally shown on both artificial and real data.