基于节点属性和结构模式的在线社交网络社区检测

B. C. Singh, Mohammad Muntasir Rahman, Md. Sipon Miah, M. K. Baowaly
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引用次数: 1

摘要

在线社交网络中的社区检测是一个困难但重要的现象,它揭示了人们之间隐藏的关系模式,从而使我们能够从社会经济学的角度理解人类的行为。社区检测算法允许我们在在线社交网络中发现这些类型的模式。识别和探测社区不仅特别重要,而且具有直接的应用。因此,近年来研究人员一直在深入研究如何实现高效的社区检测算法。本文将集合理论引入到考虑节点属性和网络结构模式的社区检测问题中。我们还建立了概率论来检测在线社交网络中的重叠社区。此外,我们将重点扩展到比较分析现有的一些社区检测方法,这些方法基本上是考虑节点属性和边缘内容来检测社区。我们对我们的框架进行全面的分析,以证明我们所建议的模型的性能。实验结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Community Detection Using Node Attributes and Structural Patterns in Online Social Networks
Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.
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