LSCPM: communities in massive real-world Link Streams by Clique Percolation Method

TL;DR

This paper introduces a scalable algorithm for community detection in massive temporal link streams using an adapted Clique Percolation Method, enabling analysis of large datasets efficiently and effectively.

Contribution

The paper presents a novel, faster algorithm for applying Clique Percolation to link streams, capable of handling datasets with millions of links.

Findings

Scales to datasets with thirty million links within twenty-five minutes

Provides communities similar to existing methods but with more aggregation

Reveals vertex importance and community relevance in real-world data

Abstract

Community detection is a popular approach to understand the organization of interactions in static networks. For that purpose, the Clique Percolation Method (CPM), which involves the percolation of k-cliques, is a well-studied technique that offers several advantages. Besides, studying interactions that occur over time is useful in various contexts, which can be modeled by the link stream formalism. The Dynamic Clique Percolation Method (DCPM) has been proposed for extending CPM to temporal networks. However, existing implementations are unable to handle massive datasets. We present a novel algorithm that adapts CPM to link streams, which has the advantage that it allows us to speed up the computation time with respect to the existing DCPM method. We evaluate it experimentally on real datasets and show that it scales to massive link streams. For example, it allows to obtain a complete set of communities in under twenty-five minutes for a dataset with thirty million links, what the state of the art fails to achieve even after a week of computation. We further show that our method provides communities similar to DCPM, but slightly more aggregated. We exhibit the relevance of the obtained communities in real world cases, and show that they provide information on the importance of vertices in the link streams.