Community Detection Based Clustering

Iveta Dirgová Luptáková, Jiri Pospíchal
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引用次数: 1

Abstract

Community detection is, in principle, a clustering of nodes based typically only on their topological properties that are derived from their positions in the network. Clustering generally uses non-topological information associated with nodes to group them. This paper uses a low-dimensional Euclidean distance of nodes to build a network (i.e. proximity or neighborhood graph) and applies community-based detection for clustering purposes. Nearest neighbors of nodes were connected by edges. Walktrap, edge betweenness, and fast greedy were used for community detection. The proposed approach generally proves superior to basic clustering methods, tested on popular 2D artificial benchmarks, and merits additional study. It also has lower computational complexity than other comparable approaches.
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基于社区检测的聚类
原则上,社区检测是节点的聚类,通常仅基于其拓扑属性,这些属性来自于它们在网络中的位置。聚类通常使用与节点相关联的非拓扑信息对节点进行分组。本文使用节点的低维欧氏距离来构建网络(即邻近图或邻域图),并采用基于社区的检测进行聚类。节点的近邻通过边连接。社区检测采用了步行陷阱、边缘间性和快速贪婪等方法。在流行的二维人工基准测试中,该方法总体上优于基本聚类方法,值得进一步研究。它的计算复杂度也比其他可比较的方法低。
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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