Cycle and flow trusses in directed networks

Author:

Takaguchi Taro12ORCID,Yoshida Yuichi13

Affiliation:

1. National Institute of Informatics, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430 Tokyo, Japan

2. JST, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430 Tokyo, Japan

3. Preferred Infrastructure, 1-6-1 Otemachi, Chiyoda-ku, 100-0004 Tokyo, Japan

Abstract

When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analysing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks.

Funder

Japan Society for the Promotion of Science

Japan Science and Technology Agency

Ministry of Education, Culture, Sports, Science, and Technology

Publisher

The Royal Society

Subject

Multidisciplinary

Reference35 articles.

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Maximal D-Truss Search in Dynamic Directed Graphs;Proceedings of the VLDB Endowment;2023-05

2. Index-based Structural Clustering on Directed Graphs;2022 IEEE 38th International Conference on Data Engineering (ICDE);2022-05

3. Modelling a multiplex brain network by local transfer entropy;Scientific Reports;2021-07-30

4. Motif-driven Dense Subgraph Discovery in Directed and Labeled Networks;Proceedings of the Web Conference 2021;2021-04-19

5. Efficient Probabilistic Truss Indexing on Uncertain Graphs;Proceedings of the Web Conference 2021;2021-04-19

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