Abstract
Nowadays, identification and detection community structures in complex networks is an important factor in extracting useful information from networks. Label Propagation Algorithm (LPA) with near linear-time complexity is one of the most popular methods for detecting community structures, yet its uncertainty and randomness is a defective factor. Merging LPA with other community detection metrics would improve its accuracy and reduce instability of LPA. Considering this point, in this paper we tried to use edge-betweenness centrality to improve LPA performance. On the other hand, calculating edge-betweenness centrality is expensive, so as an alternative metric, we use local edge-betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge-Betweenness). Experimental results on both real-world and synthetic networks show that LPA-LEB possesses higher accuracy and stability than LPA when detecting community structures in networks.
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