CoDAR:揭示社区检测的广义过程和推荐算法

Xiang Ying, Chaokun Wang, M. Wang, J. Yu, Jun Zhang
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引用次数: 2

摘要

在过去的十年里,社区检测引起了人们对图分析和挖掘的极大兴趣,并且已经开发了大量的方法来解决这个问题。然而,由于缺乏统一的框架和合理的评估方法,对大量的工作进行分析、比较和评估是一个难题,更不用说在必要的时候挑选出一个最好的。在本文中,我们设计了一个名为CoDAR的工具,它揭示了社区检测的广义过程,并在检测过程中实时监测网络的结构变化。此外,CoDAR采用了12个公认的指标,并建立了社区绩效评价的评级模型,以推荐性能最好的算法。最后,该工具还提供了很好的交互式窗口用于显示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CoDAR: Revealing the Generalized Procedure & Recommending Algorithms of Community Detection
Community detection has attracted great interest in graph analysis and mining during the past decade, and a great number of approaches have been developed to address this problem. However, the lack of a uniform framework and a reasonable evaluation method makes it a puzzle to analyze, compare and evaluate the extensive work, let alone picking out a best one when necessary. In this paper, we design a tool called CoDAR, which reveals the generalized procedure of community detection and monitors the real-time structural changes of network during the detection process. Moreover, CoDAR adopts 12 recognized metrics and builds a rating model for performance evaluation of communities to recom- mend the best-performing algorithm. Finally, the tool also provides nice interactive windows for display.
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