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Finding overlapping community from social networks based on community forest model

机译:基于社区森林模型从社交网络中查找重叠社区

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摘要

Overlapping community detection is the key research work to discover and explore the social networks. A great deal of work has been devoted to detect overlapping communities, but no one can give a clear formula definition of community from the internal structure to the external boundary. More in depth, there are four challenges to existing research works. In this paper, firstly we propose overlapping community forest model and disjoint community forest model based on the community forest model, secondly give a clear formula definition of overlapping community and disjoint community based on the backbone degree and expansion, thirdly propose a novel algorithm to find overlapping communities based on the backbone degree and expansion to resolve the four challenges. This algorithm has better performance than four related algorithms mentioned by this paper in large scale social networks. It works well on American college football, Zachary's Karate Club, Netscience-coauthor, Condensed matter collaborations, LFR etc. data sets. (C) 2016 Elsevier B.V. All rights reserved.
机译:重叠社区检测是发现和探索社交网络的关键研究工作。已经进行了大量工作来检测重叠的社区,但是没有人能够给出从内部结构到外部边界的清晰的社区公式定义。更深入地讲,现有研究工作面临四个挑战。本文首先基于社区森林模型提出了重叠社区森林模型和不相交社区森林模型,其次基于骨干度和扩展给出了清晰的重叠社区和不相交社区的公式定义,其次提出了一种新的算法来寻找基于骨干度的重叠社区和扩展来解决四个挑战。该算法比大型社交网络中本文提到的四种相关算法具有更好的性能。它在美国大学橄榄球,扎卡里的空手道俱乐部,Netscience合著者,浓缩物质合作伙伴,LFR等数据集上均能很好地工作。 (C)2016 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Knowledge-Based Systems》 |2016年第1期|238-255|共18页
  • 作者单位

    Hebei Univ Sci & Technol, Coll Informat Sci & Engn, Shijiazhuang 050018, Peoples R China|Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China;

    Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China;

    Hebei Univ Sci & Technol, Coll Informat Sci & Engn, Shijiazhuang 050018, Peoples R China|Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China;

    Hebei Univ Sci & Technol, Coll Informat Sci & Engn, Shijiazhuang 050018, Peoples R China;

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

    Community detection; Social network; Expansion; Community forest model;

    机译:社区检测;社会网络;扩展;社区森林模型;

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