Scalable Spectral Algorithms for Community Detection in Directed Networks

Abstract

Community detection has been one of the central problems in network studies and directed network is particularly challenging due to asymmetry among its links. In this paper, we found that incorporating the direction of links reveals new perspectives on communities regarding to two different roles, source and terminal, that a node plays in each community. Intriguingly, such communities appear to be connected with unique spectral property of the graph Laplacian of the adjacency matrix and we exploit this connection by using regularized SVD methods. We propose harvesting algorithms, coupled with regularized SVDs, that are linearly scalable for efficient identification of communities in huge directed networks. The proposed algorithm shows great performance and scalability on benchmark networks in simulations and successfully recovers communities in real network applications.

Single column, 40 pages, 6 figures and 7 tables

References in corpus (7)

• Modularity and community structure in networks
• Benchmark graphs for testing community detection algorithms
• Detecting the overlapping and hierarchical community structure of complex networks
• Community structure in directed networks
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Mixture models and exploratory analysis in networks
• Size reduction of complex networks preserving modularity

Cited by in corpus (4)

• Local community extraction in directed networks
• Detecting highly cyclic structure with complex eigenpairs
• Quantitative Function and Algorithm for Community Detection in Bipartite Networks
• Community Detection by $L_0$-penalized Graph Laplacian