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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >An Improved Louvain Algorithm for Community Detection
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An Improved Louvain Algorithm for Community Detection

机译：一种改进的社区检测Louvain算法

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摘要

Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and splits the local tree structure in the network. The split network is divided iteratively, then the tree structure is added to the partition results, and the results are optimized to reduce the computation. It has higher community aggregation, and the effect of community detection is improved. Through the experimental test of several groups of data, the Fast Louvain algorithm is superior to the traditional Louvain algorithm in partition effect and operation efficiency.
机译：社会网络分析在社会学、商业分析、公共安全等领域具有重要的研究意义。传统的Louvain算法是一种快速的社区检测算法，具有可靠的结果。复杂网络的规模一直在扩大，鲁汶算法的效率会越来越低。为了提高大规模网络的检测效率，该文提出一种改进的Fast Louvain算法。该算法将迭代逻辑从循环迭代优化为动态迭代，加快了收敛速度，并拆分了网络中的局部树结构。对分割网络进行迭代分割，然后将树状结构添加到分割结果中，并对结果进行优化以减少计算量。具有更高的群落聚集度，提高了群落检测效果。通过对多组数据的实验测试，Fast Louvain算法在分区效果和运算效率上均优于传统Louvain算法。

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