Interplay Between Node and Edge Laplacians Through Spectral Analysis of Redefined ‘Degree and Adjacency’ Matrices of Digraphs

This work seeks to interconnect the well-known concept of the graph Laplacian with its edge counterpart, the Edge Laplacian with respect to a new and updated definition of edge-degree and edge-adjacency matrix. It focuses on establishing spectral connections not only between these two Laplacians but also between their components, namely the degree matrices and the adjacency matrices. To achieve this interconnection, the edge degree and adjacency matrix have been redefined to not only offer a graph-theoretic interpretation of edge-based analysis but also uniquely link the corresponding edge and adjacency matrices through eigenvalue-based analysis.

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Published
2025-01-15
DOI
https://doi.org/10.1109/ica-symp63674.2025.10876548
Primary Topic
Graph theory and applications
Type
article
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article

Interplay Between Node and Edge Laplacians Through Spectral Analysis of Redefined ‘Degree and Adjacency’ Matrices of Digraphs

Dwaipayan Mukherjee, Avinash Dubey
Graph theory and applications
article

Interplay Between Node and Edge Laplacians Through Spectral Analysis of Redefined ‘Degree and Adjacency’ Matrices of Digraphs

Dwaipayan Mukherjee, Avinash Dubey
article en

Abstract

This work seeks to interconnect the well-known concept of the graph Laplacian with its edge counterpart, the Edge Laplacian with respect to a new and updated definition of edge-degree and edge-adjacency matrix. It focuses on establishing spectral connections not only between these two Laplacians but also between their components, namely the degree matrices and the adjacency matrices. To achieve this interconnection, the edge degree and adjacency matrix have been redefined to not only offer a graph-theoretic interpretation of edge-based analysis but also uniquely link the corresponding edge and adjacency matrices through eigenvalue-based analysis.

Indian Institute of Technology Bombay (IN)
Reduced inequalities
Openalex Percentile: Top 94%
Graph theory and applications
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