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An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

机译:复杂网络中一种基于拓扑-势的改进的社区检测算法

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摘要

Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node's mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.
机译:拓扑势理论是一种针对复杂网络的新的社区检测理论,它通过从每个局部最大势节点向外扩展将网络划分为社区。目前,几乎所有基于拓扑势的社区检测方法都忽略节点差异,并假设所有节点的质量相同。该假设导致拓扑电位计算的不准确性,从而降低了社区检测的精度。在PageRank算法思想的启发下,提出了一种针对复杂网络节点的大规模计算方法。通过我们的方法获得的节点质量可以有效地反映其在复杂网络中的重要性和影响。节点越重要,其质量就越大。仿真实验结果表明,考虑节点质量后,节点的拓扑电位更加准确,拓扑电位的分布更加合理,群落检测的结果更加精确。

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