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首页> 外文期刊>Neurocomputing >Tree decomposition based anomalous connected subgraph scanning for detecting and forecasting events in attributed social media networks
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Tree decomposition based anomalous connected subgraph scanning for detecting and forecasting events in attributed social media networks

机译：基于树分解的基于异常连接的子图扫描，用于检测和预测归属社交媒体网络中的事件

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摘要

Event detection and forecasting in social media networks, such as disease outbreak and air pollution event detection, have been formulated as an anomalous connected subgraph detection problem. How-ever, the huge search space and the sparsity of anomaly events make it difficult to solve this problem effectively and efficiently. This paper presents a general framework, namely anomalous connected sub -graph scanning (GraphScan) which optimizes a large class of sophisticated nonlinear nonparametric scan statistic functions, to solve this problem in attributed social media networks. We first transform the so-phisticated nonlinear nonparametric scan statistics functions into the Price-Collecting Steiner Tree (PCST) problem with provable guarantees for evaluating the significance of connected subgraphs to indicate the ongoing or forthcoming events. Then, we use tree decomposition technique to divide the whole graph into a set of smaller subgraph bags, and arrange them into a tree structure, through which we can re-duce the search space dramatically. Finally, we propose an efficient approximation algorithm to solve the problem of anomalous subgraph detection using the tree of bags. With two real-world datasets from different domains, we conduct extensive experimental evaluations to demonstrate the effectiveness and efficiency of the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.
机译：社交媒体网络（如疾病爆发和空气污染事件检测）的事件检测和预测已被制定为异常连接的子图检测问题。如何，巨大的搜索空间和异常事件的稀疏性使得难以有效且有效地解决这个问题。本文介绍了一般框架，即异常连接的子画面扫描（GraphScan），它优化了大类复杂的非线性非参数扫描统计功能，以解决其属性社交媒体网络中的此问题。我们首先将So-phisticated非线性非参数扫描统计功能转换为价格收集的施泰纳（PCST）问题，以便可提供可证明的保证，以评估连接子图的重要性，以指示正在进行的或即将到来的事件。然后，我们使用树分解技术将整个图分成一组较小的子图袋，并将它们安排到树结构中，通过该树结构，我们可以通过该树结构进行大幅重新延长搜索空间。最后，我们提出了一种有效的近似算法来解决使用袋子树的异常子图检测问题。通过来自不同域的两个现实数据，我们进行了广泛的实验评估，以证明所提出的方法的有效性和效率。 （c）2020 Elsevier B.v.保留所有权利。

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  • 来源
    《Neurocomputing》 |2020年第24期|83-93|共11页
  • 作者

    Shao Minglai; Sun Peiyuan; Li Jianxin; Yan Qiben; Feng Zhirui;

  • 作者单位

    Beihang Univ Bejing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China|Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China;

    Beihang Univ Bejing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China|Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China;

    Beihang Univ Bejing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China|Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China;

    Michigan State Univ Comp Sci & Engn E Lansing MI 48824 USA;

    Beihang Univ Bejing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China|Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R 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

    Anomalous subgraph detection; Approximation algorithm; Social media networks; Nonparametric statistics; Tree decomposition; Event detection and forecasting;

    机译：异常子图检测;近似算法;社交媒体网络;非参数统计;树分解;事件检测和预测;

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