Network Community Detection with A Successive Spectral Relaxation Method

TL;DR

This paper introduces a new spectral relaxation method for community detection in large networks, improving efficiency and scalability while maintaining high quality results.

Contribution

The authors propose a novel spectral relaxation approach with an iterative rounding and fast power method, enabling scalable community detection in large networks.

Findings

Achieves comparable or better community quality than existing methods.

Provides nearly linear speedup with increased computational nodes.

Effective for large real-world and synthetic networks.

Abstract

With invaluable theoretical and practical benefits, the problem of partitioning networks for community structures has attracted significant research attention in scientific and engineering disciplines. In literature, Newman's modularity measure is routinely applied to quantify the quality of a given partition, and thereby maximizing the measure provides a principled way of detecting communities in networks. Unfortunately, the exact optimization of the measure is computationally NP-complete and only applicable to very small networks. Approximation approaches have to be sought to scale to large networks. To address the computational issue, we proposed a new method to identify the partition decisions. Coupled with an iterative rounding strategy and a fast constrained power method, our work achieves tight and effective spectral relaxations. The proposed method was evaluated thoroughly on both real and synthetic networks. Compared with state-of-the-art approaches, the method obtained comparable, if not better, qualities. Meanwhile, it is highly suitable for parallel execution and reported a nearly linear improvement in running speed when increasing the number of computing nodes, which thereby provides a practical tool for partitioning very large networks.