The K-core of a graph is the largest subgraph within which each node has at least K connections. The key observation of this paper is that the K-core may be much smaller than the original graph while retaining its community structure. Building on this observation, we propose a framework that can accelerate community detection algorithms by first focusing on the K-core and then inferring community labels for the remaining nodes. Finally, label propagation and local modularity maximization algorithms are adopted, for their speed and quality, to optimize the community structure of the whole graph. Our experiments demonstrate that the proposed framework can reduce the running time by up to 80% while preserving the quality of the solutions. Theoretical investigations via the likelihood function based on stochastic blockmodels support our framework.
Peng et al. REF found that K-core may be much smaller than the original graph while retaining its community structure.
Accelerating Community Detection by Using K-core Subgraphs
ArXiv
ArXiv
Mathematics Physics Computer Science