Community Structure Detection in Complex Networks with Partial Background Information

Abstract

Constrained clustering has been well-studied in the unsupervised learning society. However, how to encode constraints into community structure detection, within complex networks, remains a challenging problem. In this paper, we propose a semi-supervised learning framework for community structure detection. This framework implicitly encodes the must-link and cannot-link constraints by modifying the adjacency matrix of network, which can also be regarded as de-noising the consensus matrix of community structures. Our proposed method gives consideration to both the topology and the functions (background information) of complex network, which enhances the interpretability of the results. The comparisons performed on both the synthetic benchmarks and the real-world networks show that the proposed framework can significantly improve the community detection performance with few constraints, which makes it an attractive methodology in the analysis of complex networks.

References in corpus (6)

• Modularity and community structure in networks
• Cooperative Game Theory Approaches for Network Partitioning
• Maps of random walks on complex networks reveal community structure
• Benchmark graphs for testing community detection algorithms
• Spectral methods for the detection of network community structure: a comparative analysis
• Community Structure Detection in Complex Networks with Partial Background Information

Cited by in corpus (2)

• Community Structure Detection in Complex Networks with Partial Background Information
• Community detection in bipartite networks using weighted symmetric binary matrix factorization