为了更好地了解网络,以相似度为基础,让节点选择多个相似节点形成相似节点对,通过蒙卡模拟结果提出了基于最大节点相似度和度的配对算法发现了网络的重叠社团结构。利用多级最相似度继续优化社团结构,找出了网络社团的深层重叠结构和子社团结构。提出的算法从真实网络形成社团的原因出发发现了网络的重叠结构,并且进一步优化社团结构,发现了网络的深层重叠社团结构和其中的子社团结构。
In order to better understand the network, based on the similarity, let the nodes select multiple similar nodes to form similar node pairs. Through the Monte Carlo simulation results, a pairing algorithm based on the maximum node similarity and degree is proposed to discover the overlapping community structure of the network. Using multi-level most similarity to continue to optimize the community structure, find out the deep overlapping structure and sub-community structure of the network community. The proposed algorithm discovers the overlapping structure of the network based on the reason why the real network forms a community, and further optimizes the community structure, discovering the deep overlapping community structure of the network and its sub-community structure.
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