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• W1640961073 abstract "We devise methods for finding approximations of the generalized inverse of the graph Laplacian matrix, which arises in many graph-theoretic applications. Finding this matrix in its entirety involves solving a matrix inversion problem, which is resource demanding in terms of consumed time and memory and hence impractical whenever the graph is relatively large. Our approximations use only few eigenpairs of the Laplacian matrix and are parametric with respect to this number, so that the user can compromise between effectiveness and efficiency of the approximated solution. We apply the devised approximations to the problem of computing current-flow betweenness centrality on a graph. However, given the generality of the Laplacian matrix, many other applications can be sought. We experimentally demonstrate that the approximations are effective already with a constant number of eigenpairs. These few eigenpairs can be stored with a linear amount of memory in the number of nodes of the graph and, in the realistic case of sparse networks, they can be efficientlycomputed using one of the many methods for retrieving few eigenpairs of sparse matrices that abound in the literature." @default.
• W1640961073 created "2016-06-24" @default.
• W1640961073 creator A5045692255 @default.
• W1640961073 creator A5068899786 @default.
• W1640961073 date "2012-05-22" @default.
• W1640961073 modified "2023-09-27" @default.
• W1640961073 title "Effective and efficient approximations of the generalized inverse of the graph Laplacian matrix with an application to current-flow betweenness centrality" @default.
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