The Neighbor Matrix: Generalizing the Degree Distribution

TL;DR

The paper introduces the neighborhood matrix, a new graph descriptor that generalizes the degree sequence and distance matrix, capturing comprehensive topological information useful for graph comparison and analysis.

Contribution

It presents the neighborhood matrix as a novel graph descriptor that encompasses key topological features and demonstrates its utility in graph analysis tasks.

Findings

Contains eleven common graph statistics and descriptors.

Enables comparison of graphs based on topological features.

Assists in identifying topologically significant vertices.

Abstract

The newly introduced neighborhood matrix extends the power of adjacency and distance matrices to describe the topology of graphs. The adjacency matrix enumerates which pairs of vertices share an edge and it may be summarized by the degree sequence, a list of the adjacency matrix row sums. The distance matrix shows more information, namely the length of shortest paths between vertex pairs. We introduce and explore the neighborhood matrix, which we have found to be an analog to the distance matrix what the degree sequence is to the adjacency matrix. The neighbor matrix includes the degree sequence as its first column and the sequence of all other distances in the graph up to the graph's diameter, enumerating the number of neighbors each vertex has at every distance present in the graph. We prove this matrix to contain eleven oft-used graph statistics and topological descriptors. We also provide insight into two applications that show potential utility of the neighbor matrix in comparing graphs and identifying topologically significant vertices in a graph.