Quantifying the Complexity of Nodes in Higher-Order Networks Using the Infomap Algorithm

IF 2.3 4区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Systems Pub Date : 2024-09-03 DOI:10.3390/systems12090347
Yude Fu, Xiongyi Lu, Caixia Yu, Jichao Li, Xiang Li, Qizi Huangpeng
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Abstract

Accurately quantifying the complexity of nodes in a network is crucial for revealing their roles and network complexity, as well as predicting network emergent phenomena. In this paper, we propose three novel complexity metrics for nodes to reflect the extent to which they participate in organized, structured interactions in higher-order networks. Our higher-order network is built using the BuildHON+ model, where communities are detected using the Infomap algorithm. Since a physical node may contain one or more higher-order nodes in higher-order networks, it may simultaneously exist in one or more communities. The complexity of a physical node is defined by the number and size of the communities to which it belongs, as well as the number of higher-order nodes it contains within the same community. Empirical flow datasets are used to evaluate the effectiveness of the proposed metrics, and the results demonstrate their efficacy in characterizing node complexity in higher-order networks.
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利用 Infomap 算法量化高阶网络中节点的复杂性
准确量化网络中节点的复杂性对于揭示节点的作用和网络复杂性以及预测网络突发现象至关重要。在本文中,我们为节点提出了三个新的复杂度指标,以反映它们在高阶网络中有组织、有结构的互动中的参与程度。我们的高阶网络是利用 BuildHON+ 模型构建的,其中的社群是利用 Infomap 算法检测的。由于在高阶网络中,一个物理节点可能包含一个或多个高阶节点,因此它可能同时存在于一个或多个社群中。物理节点的复杂度由其所属社区的数量和规模以及同一社区中包含的高阶节点数量来定义。我们使用经验流量数据集来评估所提出指标的有效性,结果证明了这些指标在表征高阶网络中节点复杂性方面的功效。
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来源期刊
Systems
Systems Decision Sciences-Information Systems and Management
CiteScore
2.80
自引率
15.80%
发文量
204
审稿时长
11 weeks
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