Overlapping community identification approach in online social networks
Overlapping community identification approach in online social networks
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DOI：
10.1016/j.physa.2014.10.095
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发表时间：
2015-03
影响因子：
3.3
通讯作者：
Xuewu Zhang;You Huangbin;William Zhu;Shaojie Qiao;Jianwu Li;Louis Alberto Gutierrez;Zhuo Zhang;Fan Xinnan
中科院分区：
文献类型：
--
作者：
Xuewu Zhang;You Huangbin;William Zhu;Shaojie Qiao;Jianwu Li;Louis Alberto Gutierrez;Zhuo Zhang;Fan Xinnan
在线社交网络已经深入到我们的日常生活中，以至于我们不能忽视它。人们对社交网络越来越感兴趣的一个特定领域是检测重叠社区：不要把在线社区看作是独立行动的自治岛屿，社区更像是相互渗透的蔓延城市。在线社区的行为更像复杂网络的假设带来了新的挑战，特别是在规模和复杂性方面。用于检测这些重叠社区的算法需要快速和准确。本研究提出了一种使用CNM算法来检测非重叠社区的方法，该算法反过来又允许我们推断重叠网络。此外，提出了一种改进的贴近度中心度指标来对重叠节点进行分类。在这项研究中使用的方法表现出较高的分类精度在检测重叠社区，与O（n 2）的时间复杂度。
Online social networks have become embedded in our everyday lives so much that we cannot ignore it. One specific area of increased interest in social networks is that of detecting overlapping communities: instead of considering online communities as autonomous islands acting independently, communities are more like sprawling cities bleeding into each other. The assumption that online communities behave more like complex networks creates new challenges, specifically in the area of size and complexity. Algorithms for detecting these overlapping communities need to be fast and accurate. This research proposes method for detecting non-overlapping communities by using a CNM algorithm, which in turn allows us to extrapolate the overlapping networks. In addition, an improved index for closeness centrality is given to classify overlapping nodes. The methods used in this research demonstrate a high classification accuracy in detecting overlapping communities, with a time complexity of O (n 2).