Diagonal of pseudoinverse of graph Laplacian: Fast estimation and exact results

The diagonal entries of the pseudoinverse of the Laplacian matrix of a graph appear in many important practical applications since they contain much information about the graph, and many relevant quantities can be expressed in terms of them, such as the Kirchhoff index and current flow centrality. H...

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Bibliographic Details
Published in:Theoretical computer science Vol. 1032; p. 115102
Main Authors: Lu, Zenan, Xu, Wanyue, Zhang, Zhongzhi
Format: Journal Article
Language:English
Published: Elsevier B.V 29.03.2025
Subjects:
Graph and data mining
Graph Laplacian
Kirchhoff index
Laplacian solver
Node importance
Pseudoinverse of graph Laplacian
Node importance
Graph Laplacian
Kirchhoff index
Graph and data mining
Pseudoinverse of graph Laplacian
Laplacian solver
ISSN:0304-3975
Online Access:Get full text
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Summary:The diagonal entries of the pseudoinverse of the Laplacian matrix of a graph appear in many important practical applications since they contain much information about the graph, and many relevant quantities can be expressed in terms of them, such as the Kirchhoff index and current flow centrality. However, a naïve approach for computing the diagonal of a matrix inverse has cubic computational complexity in terms of the matrix dimension, which is not acceptable for large graphs with millions of nodes. Thus, rigorous solutions to the diagonal of the Laplacian matrices for general graphs, even for particular graphs are much less. In this paper, we propose a theoretically guaranteed estimation algorithm, which approximates all diagonal entries of the pseudoinverse of a graph Laplacian in nearly linear time with respect to the number of edges in the graph. We execute extensive experiments on real-life networks, which indicate that our algorithm is both efficient and accurate. Also, we determine exact expressions for the diagonal elements of pseudoinverse of the Laplacian matrices for Koch networks and uniform recursive trees, and compare them with those obtained by our approximation algorithm. Finally, we use our algorithm to evaluate the Kirchhoff index of three deterministic model networks, for which the Kirchhoff index can be rigorously determined. These results further show the effectiveness and efficiency of our algorithm.
ISSN:0304-3975
DOI:10.1016/j.tcs.2025.115102