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Effective Semisupervised Community Detection Using Negative Information

机译：使用负信息进行有效的半监督社区检测

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摘要

The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.
机译：可以利用先验信息来指导社区结构发现过程的半监督社区检测方法在近几年引起了相当大的研究兴趣。大多数以前的工作都假定某些节点的确切标签是事先已知的，并以单个标签和成对约束的形式呈现。在本文中，我们提出了一种新型的先验信息，称为否定信息，它指示节点是否不属于特定社区。然后基于负面信息提出了半监督社区检测算法，以有效利用此类信息来辅助社区检测过程。该算法在几种人工和现实网络上进行了评估，在恢复社区方面显示出很高的效率。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第6期|109671.1-109671.8|共8页
  • 作者

    Liu Dong; Duan Dequan; Sui Shikai; Song Guojie;

  • 作者单位

    Henan Normal Univ, Sch Comp & Informat Engn, Xinxiang 453007, Peoples R China.;

    Henan Normal Univ, Sch Comp & Informat Engn, Xinxiang 453007, Peoples R China.;

    Henan Normal Univ, Sch Comp & Informat Engn, Xinxiang 453007, Peoples R China.;

    Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China.;

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