并行环境下k-Means算法在社区检测中的应用

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2015-09-21 DOI:10.1109/SYNASC.2015.54

András Bóta, Miklós Krész, Bogdán Zaválnij

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引用次数: 4

摘要

在本文中，我们给出了一个快速并行适应著名的k-均值聚类算法的初步结果。我们将使用我们的方法来检测复杂网络中的社区。出于测试目的，我们将使用Lancichinetti等人的图形生成器，并将我们的方法与OSLOM、CPM和hub渗流重叠社区检测方法进行比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Adaptations of the k-Means Algorithm to Community Detection in Parallel Environments

In this paper we present preliminary results for a fast parallel adaptation of the well-known k-means clustering algorithm to graphs. We are going to use our method to detect communities in complex networks. For testing purposes we will use the graph generator of Lancichinetti et al., and we are going to compare our method with the OSLOM, CPM, and hub percolation overlapping community detection methods.

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2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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