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Higher-Order Multiple-Feature-based Community Evolution Model with Potential Applications in Criminal Network Investigation

机译：基于高阶的多重特征的社区演变模型，潜在应用在刑事网络调查中

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摘要

Dynamic network analysis is a promising research area with a wide range of applications. Criminal network investigation is one of them. People may be interested in using dynamic network analysis to detect criminal communities in a dynamic social network, track the evolution of those communities, identify critical criminal members, or predict links between criminal members and others. One difficulty in applying dynamic network analysis to real-world data is that real-world dynamic networks may vary sparse, which can cause overfitting problem and compromise the performance of the proposed model. Another problem is that each node in a complex real-world network may have multiple features, making it complicated to compute the distance between nodes. We propose a higher-order multiple-feature-based community evolution model (HFCE) to address those two issues. The model uses a higher-order representation of the neighbouring information among nodes to alleviate the sparsity problem. It also introduces first-order similarity regularization to clarify the distance between nodes with multiple features. Experiment results show that the HFCE model outperforms five other popular dynamic network models (ESPRA, AFECT, GenLouvain, ECD and DYNMOGA) in terms of community on the real-world sparse dataset detection and link prediction precision. The HFCE model can also effectively track the evolution of the communities and identify the important nodes in the network over time, which makes it a desirable model in criminal network investigation.
机译：动态网络分析是一个有前途的研究区域，具有广泛的应用。刑事网络调查是其中之一。人们可能有兴趣使用动态网络分析来检测动态社交网络中的刑事社区，跟踪这些社区的演变，确定关键犯罪成员，或预测刑事成员与其他人之间的联系。对现实世界数据应用动态网络分析的一个难度是真实世界的动态网络可能会稀疏，这可能导致过度的问题并损害所提出的模型的性能。另一个问题是复杂的真实网络中的每个节点可以具有多个功能，使其复杂化以计算节点之间的距离。我们提出了一个高阶多特征的社区演进模型（HFCE）来解决这两个问题。该模型使用节点之间的邻近信息的高阶表示来缓解稀疏问题。它还介绍了一定的相似性正则化，以阐明具有多个功能的节点之间的距离。实验结果表明，在真实世界稀疏数据集检测和链路预测精度方面，HFCE模型在社区方面优于5个其他流行的动态网络模型（ESPRA，AFECT，Genlouvain，ECD和Dynmoga）。 HFCE模型还可以有效地跟踪社区的演变，并随时间识别网络中的重要节点，这使其成为犯罪网络调查中的理想模型。

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  • 来源
    《Future generation computer systems》 |2021年第12期|364-375|共12页
  • 作者

    Xiaoming Li; Guangquan Xu; Changzheng Liu; Wei Yu; Zhao Liu; Zhenhuan Wu; Xiaoping Yang;

  • 作者单位

    School of International Business Zhejiang Yuexiu University Shaoxing China;

    School of Big Data Qingdao Huanghai University Qingdao China Tianjin Key Laboratory of Advanced Networking(TANK) College of Intelligence and Computing Tianjin University Tianjin China;

    College of Information Science and Technology Shihezi University Shihezi China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China;

    School of International Business Zhejiang Yuexiu University Shaoxing China Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart Society Shaoxing China;

    Research Center for New Crimes People's Public Security University of China Beijing China;

    Information Center of Ministry of Justice of the People's Republic of China Beijing China;

    School of International Business Zhejiang Yuexiu University Shaoxing China Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart Society Shaoxing China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Crime community; Higher-order neighboring information; Non-negative Matrix Factorization; First-order similarity regularization; Criminal network investigation;

    机译：犯罪群落;高阶邻近信息;非负矩阵分解;一阶相似性正则化;刑事网络调查;

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