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Overlapping community detection based on node location analysis

机译:基于节点位置分析的重叠社区检测

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摘要

As a novel overlapping community detection theory, topology potential has inspired many methods. However, these methods ignore the mass difference between nodes, leading to inaccurate topological potential values of nodes. Moreover, additional strategies are needed to determine the community affiliation of nodes, further complicating the process of community detection. In this paper, we propose a new overlapping community detection method based on node location analysis. In the proposed method, the PageRank algorithm is used to evaluate the node mass, and the community affiliation of nodes is determined based on their positions in the inherent peak-valley structure of the topology potential field. Experimental results show that the proposed method exhibits excellent performance on artificial and real-world networks and outperforms other topology-potential-based and most non-topology-potential based methods. (C) 2016 Elsevier B.V. All rights reserved.
机译:作为一种新颖的重叠社区检测理论,拓扑潜力激发了许多方法。但是,这些方法忽略了节点之间的质量差异,从而导致节点的拓扑潜力值不准确。此外,还需要其他策略来确定节点的社区隶属关系,从而使社区检测过程进一步复杂化。本文提出了一种基于节点位置分析的重叠社区检测新方法。在提出的方法中,使用PageRank算法评估节点质量,并根据节点在拓扑势场的固有峰谷结构中的位置确定节点的社区隶属关系。实验结果表明,该方法在人工和现实网络中均具有优异的性能,并且优于其他基于拓扑势和大多数非拓扑势的方法。 (C)2016 Elsevier B.V.保留所有权利。

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