A Note on Short-Range Network Communication and Clustering

Complex systems of interacting components often can be modeled by a graph that consists of a set of n nodes and a set of m edges. Such a graph can be represented by an adjacency matrix A∈Rn×n, whose (ij)th entry is one if there is an edge pointing from node i to node j, and is zero otherwise. The matrix A and its low-order powers reveal important properties of the graph and allow the enumeration of short paths and cycles that are important for determining short-range communication in the graph as well as node clustering. Closed-form expressions for path matrices of length up to four are derived, and a novel indicator of the structural propensity of the graph to form clusters is proposed. Numerical examples illustrate our analysis.

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Publication Details

Journal
Mathematics
Published
2026-09-01
DOI
https://doi.org/10.3390/math14173143
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

A Note on Short-Range Network Communication and Clustering

Silvia Noschese, M.T. Darvishi, Lothar Reichel, Najaya Al-Hajri
Mathematics
Complex Network Analysis Techniques
article

A Note on Short-Range Network Communication and Clustering

Silvia Noschese, M.T. Darvishi, Lothar Reichel, Najaya Al-Hajri
article en

Abstract

Complex systems of interacting components often can be modeled by a graph that consists of a set of n nodes and a set of m edges. Such a graph can be represented by an adjacency matrix A∈Rn×n, whose (ij)th entry is one if there is an edge pointing from node i to node j, and is zero otherwise. The matrix A and its low-order powers reveal important properties of the graph and allow the enumeration of short paths and cycles that are important for determining short-range communication in the graph as well as node clustering. Closed-form expressions for path matrices of length up to four are derived, and a novel indicator of the structural propensity of the graph to form clusters is proposed. Numerical examples illustrate our analysis.

MathematicsVol. 14(17)
Razi University (IR), Kent State University (US), Sapienza University of Rome (IT)
European Commission
Openalex Percentile: Top 10%
Complex Network Analysis Techniques
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