ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (1): 70-82.doi: 10.7544/issn1000-1239.2021.20190775

• 人工智能 • 上一篇    下一篇


刘海姣1,马慧芳1,2,赵琪琪1, 李志欣2   

  1. 1(西北师范大学计算机科学与工程学院 兰州 730070);2(广西多源信息挖掘与安全重点实验室(广西师范大学) 广西桂林 541004) (
  • 出版日期: 2021-01-01
  • 基金资助: 

Target Community Detection with User Interest Preferences and Influence

Liu Haijiao1, Ma Huifang1,2, Zhao Qiqi1, Li Zhixin2   

  1. 1(College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070);2(Guangxi Key Laboratory of Multi-Source Information Mining and Security (Guangxi Normal University), Guilin, Guangxi 541004)
  • Online: 2021-01-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61762078, 61363058,61966004), the Research Fund of Guangxi Key Laboratory of Multi-Source Information Mining & Security (MIMS18-08), and the Research Fund of Northwest Normal University Young Teachers Research Capacity Promotion Plan (NWNU-LKQN2019-2).

摘要: 目标社区检测旨在找到符合用户偏好的有凝聚力的社区.然而,所有现有工作要么在很大程度上忽视社区的外部影响,要么不是"基于目标的",即不适合目标请求.为了解决这一问题,提出面向属性网络的融合用户兴趣偏好与社区影响力的目标社区发现方法,挖掘与用户偏好相关且最具一定影响力的高质量社区.首先,综合节点结构与属性信息,挖掘包含样例节点的极大k-团作为潜在目标社区核心,并设计熵加权属性权重计算方法来捕获潜在目标社区属性子空间权重,挖掘用户偏好;其次,融合社区内部紧密性和外部可分离性定义社区质量函数,以极大k-团为核心扩展得到高质量的潜在目标社区;最后,定义社区的外部影响分数量化办法,并结合社区质量函数值及外部影响分数对所有潜在目标社区排序,输出综合质量较高的社区为目标社区.此外,在计算极大k-团的属性子空间权重时,设计了2重剪枝策略提升方法的性能和效率.在人工网络和真实网络数据集上的实验结果印证了所提方法的效率和有效性.

关键词: 用户兴趣偏好, 极大k-团, 属性子空间, 社区影响力, 目标社区发现

Abstract: Target community detection is to find the cohesive communities consistent with user’s preference. However, all the existing works either largely ignore the outer influence of the communities, or not “target-based”, i.e., they are not suitable for a target request. To solve the above problems, in this paper, the target community detection with user interest preferences and influence (TCPI) is proposed to locate the most influential and high-quality community related to user’s preference. Firstly, the node structure and attribute information are synthesized, and maximum k-cliques containing sample nodes are investigated as the core of the potential target community, and an entropy weighted attribute weight calculation method is designed to capture the attribute subspace weight of the potential target community. Secondly, the internal compactness and the external separability of the community is defined as the community quality function and the high-quality potential target community is expanded with each of the maximum k-cliques as the core. Finally, the external impact score of the community is defined, and all potential target communities are ranked according to the quality function and the external impact score of the community, and the communities with higher comprehensive quality are decided as the target communities. In addition, a pruning strategy of two-level is designed to improve the performance and efficiency of the algorithm after calculating the attribute subspace weights of all maximal k-cliques. Experimental results on synthetic networks and real-world network datasets verify the efficiency and effectiveness of the proposed method.

Key words: user interest preference, maximal k-clique, attribute subspace, community influence, target community detection