LPA Based Hierarchical Community Detection

Community structure has many practical applications, and identifying communities could help us to understand and exploit networks more effectively. Generally, real-world networks often have hierarchical structures with communities embedded within other communities. However, there are few effective methods can identify these structures. This paper proposes an algorithm HELPA to detect hierarchical community structures. HELPA is based on coreness centrality to update node's possible community labels, and uses communities as nodes to build super-network. By repeat the procedure, the proposed algorithm can effectively reveal hierarchical communities with different size in various network scales. Moreover, it overcomes the high complexity and poor applicability problem of similar algorithms. To illustrate our methodology, we compare it with many classic methods in real-world networks. Experimental results demonstrate that HELPA achieves excellent performance.

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