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首页> 外文期刊>IEEE systems journal >K-Clique Community Detection in Social Networks Based on Formal Concept Analysis
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K-Clique Community Detection in Social Networks Based on Formal Concept Analysis

机译：基于形式概念分析的社交网络K-Clique社区检测

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摘要

With the advent of ubiquitous sensing and networking, future social networks turn into cyber-physical interactions, which are attached with associated social attributes. Therefore, social network analysis is advancing the interconnections among cyber, physical, and social spaces. Community detection is an important issue in social network analysis. Users in a social network usually have some social interactions with their friends in a community because of their common interests or similar profiles. In this paper, an efficient algorithm of k-clique community detection using formal concept analysis (FCA)—a typical computational intelligence technique, namely, FCA-based k-clique community detection algorithm, is proposed. First, a formal context is constructed from a given social network by a modified adjacency matrix. Second, we define a type of special concept named k-equiconcept, which has the same k-size of extent and intent in a formal concept lattice. Then, we prove that the k-clique detection problem is equivalent to finding the k-equiconcepts. Finally, the efficient algorithms for detecting the k-cliques and k-clique communities are devised by virtue of k-equiconcepts and k-intent concepts, respectively. Experimental results demonstrate that the proposed algorithm has a higher F-measure value and significantly reduces the computational cost compared with previous works. In addition, a correlation between k and the number of k-clique communities is investigated.
机译：随着无处不在的传感和网络的出现，未来的社交网络变成了网络与物理的交互，并伴随着相关的社会属性。因此，社交网络分析正在推进网络，物理和社交空间之间的互连。社区检测是社交网络分析中的重要问题。社交网络中的用户通常由于其共同兴趣或相似的个人资料而与社区中的朋友进行一些社交互动。本文提出了一种使用形式概念分析（FCA）的高效的k族群落检测算法-一种典型的计算智能技术，即基于FCA的k族群落检测算法。首先，通过修改后的邻接矩阵从给定的社交网络构建正式上下文。其次，我们定义了一种特殊概念k-equiconcept，它在形式概念格中具有相同的k大小范围和意图。然后，我们证明了k爬坡检测问题等同于找到k个对立概念。最后，分别借助k个概念和k个意图概念，设计了用于检测k形和k形社区的有效算法。实验结果表明，与以前的工作相比，该算法具有较高的F度量值，并显着降低了计算量。此外，还研究了k与k族群落数量之间的相关性。

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  • 来源
    《IEEE systems journal》 |2017年第1期|250-259|共10页
  • 作者

    Fei Hao; Geyong Min; Zheng Pei; Doo-Soon Park; Laurence T. Yang;

  • 作者单位

    Department of Computer Software Engineering, Soonchunhyang University, Asan, Korea;

    College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, U.K.;

    Center for Radio Administration and Technology Development, Xihua University, Chengdu, China;

    Department of Computer Software Engineering, Soonchunhyang University, Asan, Korea;

    Department of Computer Science, School of Computer Science and Technology, Huazhong University of Science and Technology, St. Francis Xavier University, Wuhan, Antigonish, NS, ChinaCanada;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Communities; Social network services; Context; Lattices; Time complexity; Bismuth; Formal concept analysis;

    机译：社区;社交网络服务;上下文;格;时间复杂度;铋;形式概念分析;

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