Community structure analysis using label propagation and flow-based ensemble learning

Network is a powerful paradigm for representing complex relationships and finding the community structure of networks can help people better understand the real world. Infomap, which employs the minimum description length as the optimization objective, is a competent algorithm for community structure analysis. In this paper, we propose a novel algorithm combining flow-based ensemble learning and Label Propagation Algorithm (LPA). Firstly, Infomap (without recursive steps) is incorporated into Core Groups Graph Clustering (CGGC), an ensemble learning framework for community detection. Next, the output of CGGC-Infomap is used as the input of LPA, which can make LPA converge to highly stable clustering results. Experimental studies show that our algorithm can achieve better performance in terms of Normalized Mutual Information (NMI) and requires less memory than the original Infomap algorithm. Our method also features good parallelism, making it potentially more suitable for processing large scale networks.