Insights on structure and influence from the adjacency and Laplacian eigenspectra of intersecting ring networks

A network's community structure commonly impacts its functions. For instance, networks seeking synchronization will see this process follow the topology's hierarchical and community structuring. Herein, the interplay of network adjacency and Laplacian eigenspectra is shown to uncover hierarchical influence and community structure. Ring networks embedded with hubs of high connectivity are first analyzed to characterize the differing insights of the adjacency and Laplacian eigenspectra. This understanding is then transferred to the case study of satellite networks composed of intersecting rings. From these scenarios, it is found that where the adjacency reflects a network's structure, the Laplacian detects nodes' influence profiles. The adjacency identifies the number and relative sizes of salient network structures, for example hubs of high connectivity. In contrast, the Laplacian decomposes these structures in sets of nodes with equivalent influence profiles, where influence emerges due to variations in nodes' connectivity.

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Publication Details

Journal
Physical review. E
Published
2026-02-23
DOI
https://doi.org/10.1103/4595-wyvd
Primary Topic
Graph theory and applications
Type
article
Field-Weighted Citation Impact
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article

Insights on structure and influence from the adjacency and Laplacian eigenspectra of intersecting ring networks

Anonymous
Physical review. E
Graph theory and applications
article

Insights on structure and influence from the adjacency and Laplacian eigenspectra of intersecting ring networks

Anonymous
article en

Abstract

A network's community structure commonly impacts its functions. For instance, networks seeking synchronization will see this process follow the topology's hierarchical and community structuring. Herein, the interplay of network adjacency and Laplacian eigenspectra is shown to uncover hierarchical influence and community structure. Ring networks embedded with hubs of high connectivity are first analyzed to characterize the differing insights of the adjacency and Laplacian eigenspectra. This understanding is then transferred to the case study of satellite networks composed of intersecting rings. From these scenarios, it is found that where the adjacency reflects a network's structure, the Laplacian detects nodes' influence profiles. The adjacency identifies the number and relative sizes of salient network structures, for example hubs of high connectivity. In contrast, the Laplacian decomposes these structures in sets of nodes with equivalent influence profiles, where influence emerges due to variations in nodes' connectivity.

Physical review. EVol. 113(3)
Openalex Percentile: Top 43%
Graph theory and applications
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