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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion
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Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion

机译：使用邻域膨胀种子扩展的重叠社区检测

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摘要

Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. An important step in our method is the neighborhood inflation step where seeds are modified to represent their entire vertex neighborhood. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks.
机译：社区检测是网络分析中的重要任务。社区（也称为群集）是一组内聚顶点，内聚联系多于外聚联系。在许多社会和信息网络中，这些社区自然会重叠。例如，在社交网络中，图中的每个顶点对应于通常参与多个社区的个人。在本文中，我们提出了一种使用种子扩展方法的有效重叠社区检测算法。我们算法的关键思想是找到好的种子，然后根据社区指标贪婪地扩展这些种子。在这种种子扩展方法中，我们研究了如何确定图中的良好种子节点的问题。特别是，我们针对个性化PageRank聚类方案开发了新的播种策略，该方案可优化电导社区得分。我们方法中的一个重要步骤是邻域膨胀步骤，其中修改种子以代表其整个顶点邻域。实验结果表明，我们的种子扩展算法在产生粘性聚类和识别真相社区方面优于其他最新的重叠社区检测方法。我们还表明，我们的新播种策略优于现有策略，因此可以有效地在现实世界网络中找到良好的重叠社区。

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