Role-based Label Propagation Algorithm for Community Detection
arXiv:1601.06307
Abstract
Community structure of networks provides comprehensive insight into their organizational structure and functional behavior. LPA is one of the most commonly adopted community detection algorithms with nearly linear time complexity. But it suffers from poor stability and occurrence of monster community due to the introduced randomize. We note that different community-oriented node roles impact the label propagation in different ways. In this paper, we propose a role-based label propagation algorithm (roLPA), in which the heuristics with regard to community-oriented node role were used. We have evaluated the proposed algorithm on both real and artificial networks. The result shows that roLPA is comparable to the state-of-the-art community detection algorithms.
20 pages, 7 figures
References in corpus (14)
- Community detection in graphs
- Finding community structure in networks using the eigenvectors of matrices
- Cooperative Game Theory Approaches for Network Partitioning
- Maps of random walks on complex networks reveal community structure
- Near linear time algorithm to detect community structures in large-scale networks
- Benchmark graphs for testing community detection algorithms
- Resolution limit in community detection
- Comparing community structure identification
- Detecting network communities by propagating labels under constraints
- Towards real-time community detection in large networks
- Advanced modularity-specialized label propagation algorithm for detecting communities in networks
- Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction
- Note on the equivalence of the label propagation method of community detection and a Potts model approach
- On the relationship between Gaussian stochastic blockmodels and label propagation algorithms