Higher-order structure based node importance evaluation in directed networks

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Processing & Management Pub Date : 2024-11-04 DOI:10.1016/j.ipm.2024.103948
Meng Li , Zhigang Wang , An Zeng , Zengru Di
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Abstract

Evaluating the significance of objects with possible relevant information is a crucial topic in information science. Due to the fact that objects related to each other can often be described using complex networks, this topic also forms a fundamental theme in network science. Most traditional methods for characterizing the importance of nodes in complex networks only utilize the binary relationships between node pairs, neglecting the influence brought by higher-order structures. Considering the specific interaction modes between local nodes in the network, this paper associates the higher-order structural characteristics of the network with the importance of the nodes. It constructs an evaluation framework for the importance of nodes in directed networks based on higher-order structures. Experimental analysis on both artificial data and scientific citation data from the APS dataset has validated the effectiveness of the proposed algorithms. Compared with PageRank and eigenvector centrality, the proposed algorithms demonstrated higher accuracy, revealing the role of higher-order structures in node importance evaluation. Finally, a robustness analysis of several algorithms indicated that the proposed algorithms exhibited good robustness.
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基于高阶结构的有向网络节点重要性评估
评估具有可能相关信息的对象的重要性是信息科学的一个重要课题。由于相互关联的对象通常可以用复杂网络来描述,因此这一课题也构成了网络科学的一个基本主题。表征复杂网络中节点重要性的传统方法大多只利用节点对之间的二元关系,忽略了高阶结构带来的影响。考虑到网络中局部节点之间的特定交互模式,本文将网络的高阶结构特征与节点的重要性联系起来。本文构建了一个基于高阶结构的有向网络节点重要性评估框架。对人工数据和来自 APS 数据集的科学引文数据的实验分析验证了所提算法的有效性。与 PageRank 和特征向量中心性相比,所提出的算法具有更高的准确性,揭示了高阶结构在节点重要性评价中的作用。最后,对几种算法的鲁棒性分析表明,所提出的算法具有良好的鲁棒性。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
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