The Forest Metrics for Graph Vertices
arXiv:math/0602573 · doi:10.1016/S1571-0653(04)00058-7
Abstract
We propose a new graph metric and study its properties. In contrast to the standard distance in connected graphs, it takes into account all paths between vertices. Formally, it is defined as d(i,j)=q_{ii}+q_{jj}-q_{ij}-q_{ji}, where q_{ij} is the (i,j)-entry of the {\em relative forest accessibility matrix} Q(ε)=(I+εL)^{-1}, L is the Laplacian matrix of the (weighted) (multi)graph, and εis a positive parameter. By the matrix-forest theorem, the (i,j)-entry of the relative forest accessibility matrix of a graph provides the specific number of spanning rooted forests such that i and j belong to the same tree rooted at i. Extremely simple formulas express the modification of the proposed distance under the basic graph transformations. We give a topological interpretation of d(i,j) in terms of the probability of unsuccessful linking i and j in a model of random links. The properties of this metric are compared with those of some other graph metrics. An application of this metric is related to clustering procedures such as "centered partition." In another procedure, the relative forest accessibility and the corresponding distance serve to choose the centers of the clusters and to assign a cluster to each non-central vertex. The notion of cumulative weight of connections between two vertices is proposed. The reasoning involves a reciprocity principle for weighted multigraphs. Connections between the resistance distance and the forest distance are established.
14 pages, 19 ref
References in corpus (1)
Cited by in corpus (10)
- Forest matrices around the Laplacian matrix
- Developments in the theory of randomized shortest paths with a comparison of graph node distances
- A Class of Graph-Geodetic Distances Generalizing the Shortest-Path and the Resistance Distances
- Spanning Forests and the Golden Ratio
- Matrices of Forests and the Analysis of Digraphs
- Matrices of forests, analysis of networks, and ranking problems
- How to choose the most appropriate centrality measure? A decision tree approach
- The Laplacian Spectra of Graphs and Complex Networks
- The Shortest-Path distance on graphons
- A generalized inverse for graphs with absorption