基于种子扩展和图最短路径的重叠社区发现

Wenzhang Wang, Xiaoyan Zheng
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引用次数: 0

摘要

本文提出了一种基于种子扩展和图最短路径相结合的重叠社团发现算法(基于种子扩展和图最短路径的重叠社团检测,以下简称SESPG算法)。首先,根据节点的影响选择种子节点。其次,通过计算节点与种子群落的相似度,对群落节点进行初步划分;然后计算群落内种子与群落内节点之间最短路径的最大值。用这个作为半径。对于半径内但不在社区内的节点,计算与社区的相似度,超过阈值则加入社区。最后将相似度较高的群落进行合并,完成重叠群落的发现。经过与其他算法的比较,SESPG算法在复杂度高、节点数量大的情况下具有较好的性能。
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Overlapping Community Discovery based on Seed Expansion and Shortest Path of Graph
This article proposes an overlapping community discovery algorithm based on the combination of seed extension and the shortest path of the graph (Overlapping Communities Detecting Based on Seed Extension and Shortest Path of Graph, the following referred to as SESPG algorithm). First, the seed node is selected according to the influence of the node. Secondly, the nodes of the community are preliminarily divided by calculating the similarity between the nodes and the seed community. Then calculate the maximum value of the shortest path between the seeds in the community and the nodes in the community. Use this as the radius. For nodes within the radius but not in the community, calculate the similarity with the community, and join the community if it exceeds the threshold. Finally, the communities with high similarity are merged to complete the discovery of overlapping communities. After comparing with other algorithms, the SESPG algorithm has a good performance when the complexity is high, and the number of nodes is large.
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