基于流行病模型的网络影响节点排序方法:排名合理性视角

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-03-19 DOI:10.1145/3653296
Bing Zhang, Xuyang Zhao, Jiangtian Nie, Jianhang Tang, Yuling Chen, Yang Zhang, Dusit Niyato
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引用次数: 0

摘要

现有的影响节点排序方法(INRMs)调查和评论主要关注技术细节,而忽视了对验证这些节点在网络中的实际影响力的深入研究。这种疏忽可能会导致错误的排名。在本调查中,我们对过去 20 年中基于流行病模型的 82 项与 INRM 相关的主要研究进行了广泛分析,从而弥补了这一不足。我们通过统计分析将基准网络分为四种类型,并得出结论:具有完整信息的高质量网络是不够的,大多数 INRM 只关注无向和非加权网络,这鼓励业界和学术界合作提供优化网络。此外,我们还对七类 INRM 的优缺点和优化工艺进行了分类和讨论,帮助工程师和研究人员在根据具体需求选择合适的 INRM 时缩小选择范围。此外,我们还定义了能力和正确性指标,并利用它们的使用频率和功能来帮助工程师和研究人员为不同的 INRM 和网络优先选择合适的指标。据我们所知,这是第一份系统总结 INRM 的能力和正确性的调查报告,为复杂网络社区提供了深入见解,并有助于 INRM 的选择和评估。
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Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Existing surveys and reviews on Influential Node Ranking Methods (INRMs) have primarily focused on technical details, neglecting thorough research on verifying the actual influence of these nodes in a network. This oversight may result in erroneous rankings. In this survey, we address this gap by conducting an extensive analysis of 82 primary studies related to INRMs based on the epidemic model over the past 20 years. We statistically analyze and categorize benchmark networks into four types, and conclude that high-quality networks with complete information are insufficient and most INRMs only focus on undirected and unweighted networks, which encourages collaboration between industry and academia to provide optimized networks. Additionally, we categorize and discuss the strengths, weaknesses, and optimized crafts of seven categories of INRMs, helping engineers and researchers narrow down their choices when selecting appropriate INRMs for their specific needs. Furthermore, we define the Capability and Correctness metrics and utilize their usage frequency and functionality to assist engineers and researchers in prioritizing and selecting suitable metrics for different INRMs and networks. To our knowledge, this is the first survey that systematically summarizes the Capability and Correctness of INRMs, providing insights for the complex network community and aiding INRM selection and evaluation.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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