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A fuzzy social network centrality analysis model for interpersonal spatial relations

机译:人际空间关系的模糊社交网络集中度分析模型

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摘要

Interpersonal nodes in the social networking sites reflect the para-virtual and para-real relationships, and form a complex social network. To reveal the properties of the interpersonal nodes and their interrelations effectively, this paper develops an evaluation index system, which contains fuzzy comentropy, fuzzy node degree, fuzzy condensation degree, fuzzy cluster coefficient and fuzzy geographic concentration, and particularly proposes a fuzzy social network centrality analysis (FSNCA) model, which combines "node distribution", "node connection strength" and "node condensation and cluster", based on the fuzzy graph-theory. The FSNCA model has been successfully applied to study the interpersonal nodes' spatial relations in three social networking services (SNS) cases, in which three progressive layers, "nodes", "node connections" and "differences between node connections", are analyzed respectively. In the first case, the "friend group nodes" are studied to evaluate the equilibrium of node distribution in the network space by use of the fuzzy comentropy index. In the second case, the "connections between BBS group regional nodes" are studied to evaluate the centrality status and types of nodes in the network space by use of the fuzzy node degree index. The third case analyzes the "node connections of two kinds of chance relation groups" to evaluate the differences of centrality status in different networks by use of the fuzzy condensation degree, fuzzy cluster coefficient and fuzzy geographic concentration indexes. The systematic cognition of the interpersonal node spatial relations in the SNS community proves that the proposed method can effectively reveal the essence of fuzzy centralities. It sheds some light on the para-virtual and para-real geographical research, which is of significance to enrich the para-virtual and para-real geographical spatial relation theory, and it can also directly support the management and development of social networks of online services. (C) 2016 Elsevier B.V. All rights reserved.
机译:社交网站中的人际节点反映了准虚拟和准真实的关系,并形成了一个复杂的社交网络。为了有效地揭示人际交往节点的属性及其相互关系,本文建立了一个评价指标体系,该评价指标体系包括模糊共度,模糊节点度,模糊凝聚度,模糊聚类系数和模糊地理集中度,特别提出了模糊社会网络的中心性。分析(FSNCA)模型,基于模糊图论,它结合了“节点分布”,“节点连接强度”和“节点凝结与聚类”。 FSNCA模型已成功地应用于研究三种社交网络服务(SNS)情况下人际节点的空间关系,其中分别分析了三个渐进层,即“节点”,“节点连接”和“节点连接之间的差异”。 。在第一种情况下,研究“朋友组节点”以通过使用模糊comentropy指数评估网络空间中节点分布的平衡。在第二种情况下,研究“ BBS组区域节点之间的连接”以通过使用模糊节点度指数来评估网络空间中节点的中心状态和类型。第三种情况是通过模糊凝结度,模糊聚类系数和模糊地理集中度指数分析“两种机会关系组的节点连接”来评估不同网络中中心状态的差异。对SNS社区中人际节点空间关系的系统认识证明,该方法可以有效地揭示模糊中心性的本质。为准虚拟和准真实的地理研究提供了一些启示,对于丰富准虚拟和准真实的地理空间关系理论具有重要意义,也可以直接支持在线社交网络的管理和发展。服务。 (C)2016 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Knowledge-Based Systems》 |2016年第8期|206-213|共8页
  • 作者单位

    Hebei Normal Univ, Sch Tourism, Shijiazhuang 050024, Hebei, Peoples R China;

    Hebei Normal Univ, Sch Tourism, Shijiazhuang 050024, Hebei, Peoples R China;

    Hebei Normal Univ, Sch Tourism, Shijiazhuang 050024, Hebei, Peoples R China;

    Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Decis Syst & E Serv Intelligence Lab, Sydney, NSW, Australia;

    Hebei Normal Univ, Sch Tourism, Shijiazhuang 050024, Hebei, Peoples R China;

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

    Social network; Fuzzy analysis model; Fuzzy clustering; Social network analysis;

    机译:社交网络;模糊分析模型;模糊聚类;社交网络分析;

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