Accelerating Community Detection by Using K-core Subgraphs

TL;DR

This paper introduces a framework that accelerates community detection in large graphs by leveraging K-core subgraphs, significantly reducing computation time while maintaining solution quality.

Contribution

The paper presents a novel framework combining K-core subgraphs with existing community detection algorithms to improve efficiency without sacrificing accuracy.

Findings

Reduces community detection runtime by over 80%.

Maintains high-quality community detection results.

Supports using K-core as an effective reduced graph representation.

Abstract

Community detection is expensive, and the cost generally depends at least linearly on the number of vertices in the graph. We propose working with a reduced graph that has many fewer nodes but nonetheless captures key community structure. The K-core of a graph is the largest subgraph within which each node has at least K connections. We propose a framework that accelerates community detection by applying an expensive algorithm (modularity optimization, the Louvain method, spectral clustering, etc.) to the K-core and then using an inexpensive heuristic (such as local modularity maximization) to infer community labels for the remaining nodes. Our experiments demonstrate that the proposed framework can reduce the running time by more than 80% while preserving the quality of the solutions. Recent theoretical investigations provide support for using the K-core as a reduced representation.