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A Modularity Maximization Algorithm for Community Detection in Social Networks with Low Time Complexity

机译:低时间复杂度的社交网络社区检测模块最大化算法

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摘要

Graph vertices are often divided into groups or communities with dense connections within communities and sparse connections between communities. Community detection has recently attracted considerable attention in the field of data mining and social network analysis. Existing community detection methods require too much space and are very time consuming for moderate-to-large networks, whereas large-scale networks have become ubiquitous in real world. We proposed a method that can find communities of a graph with good time and space complexity and good accuracy as well.
机译:图顶点通常被分为群体或群体,群体内部的联系紧密,而社区之间的联系稀疏。社区检测最近在数据挖掘和社交网络分析领域引起了相当大的关注。现有的社区检测方法需要大量空间,并且对于中大型网络而言非常耗时,而大型网络已在现实世界中变得无处不在。我们提出了一种方法,该方法可以找到具有良好的时间和空间复杂度以及良好的准确性的图社区。

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