Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods.

中文翻译：

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通过度中心性和扩展在折线图上揭示局部社区结构

局部社区检测算法是一种重要的重叠社区检测方法。本地社区检测方法通过搜索种子和扩展过程来识别本地社区结构。在本文中，我们提出了一种新的基于度中心性和扩展的折线图局部社区检测方法（LCDDCE）。我们首先采用线图模型将边转移到新图的节点中。其次，我们通过一种新的折线图节点相似性方法来评估边关系。第三，我们引入局部社区检测框架，结合度中心性和PageRank算法识别线图的局部节点社区结构。最后，我们将它们转移回原始图。