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  Community structure unveils the path multiplicity in complex networks

Deng, Y., Wu, J., Lu, X., Holme, P., Li, D., Di, Z., Chen, G., Kurths, J. (2026): Community structure unveils the path multiplicity in complex networks. - Nature Communications, 17, 2283.
https://doi.org/10.1038/s41467-026-70369-4

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 Creators:
Deng, Ye1, Author
Wu, Jun1, Author
Lu, Xin1, Author
Holme, Petter1, Author
Li, Daqing1, Author
Di, Zengru1, Author
Chen, Guanrong1, Author
Kurths, Jürgen2, Author           
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Networks with complex topologies describe numerous natural and social systems. Recent studies on path multiplicity have shown strong heterogeneity in shortest paths between node pairs in real-world networks. However, the mechanism underlying this phenomenon remains unexplored. Here, we reveal that community structure is a key factor shaping path multiplicity. To explore the intrinsic factors that influence path multiplicity, we first introduce the concept of relative path multiplicity and find that community structure is more strongly correlated with path multiplicity than other network metrics. Through targeted edge-rewiring experiments, we verify the link between path multiplicity and community structure. The underlying mechanism can be interpreted as an interface-driven effect that sharply increases the number of shortest paths. Inspired by these findings, we propose a tribal-structure-based network model that reproduces phenomena observed in real-world networks. Our work enhances the understanding of network organization, with potential applications in network design and optimization.

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Language(s): eng - English
 Dates: 2026-03-062026-03-06
 Publication Status: Finally published
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41467-026-70369-4
MDB-ID: No MDB - stored outside PIK (see locators/paper)
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
OATYPE: Gold Open Access
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
 Degree: -

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Title: Nature Communications
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 17 Sequence Number: 2283 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354
Publisher: Nature