The subgraph eigenvector centrality of graphs

TL;DR

This paper introduces a new subgraph-based eigenvector centrality measure for graphs, generalizing classical centralities and capable of distinguishing vertices in regular graphs.

Contribution

It proposes the $F$-subgraph tensor and eigenvector centrality, extending centrality concepts to subgraph structures and proving their existence under certain conditions.

Findings

The $F$-subgraph eigenvector centrality generalizes classical eigenvector centrality.

It can distinguish vertices in regular graphs where traditional measures cannot.

The new centralities differ from classic ones in vertex ranking results.

Abstract

Let $G$ be a connected graph and let $F$ be a connected subgraph of $G$ with a given structure. We consider that the centrality of a vertex $i$ of $G$ is determined by the centrality of other vertices in all subgraphs contain $i$ and isomorphic to $F$. In this paper we propose an $F$-subgraph tensor and an $F$-subgraph eigenvector centrality of $G$. When the graph is $F$-connected, we show that the $F$-subgraph tensor is weakly irreducible, and in this case, the $F$-subgraph eigenvector centrality exists. Specifically, when we choose $F$ to be a path $P_1$ of length $1$(or a complete graph $K_2$), the $F$-eigenvector centrality is eigenvector centrality of $G$. Furthermore, we propose the $(K_2,F)$-subgraph eigenvector centrality of $G$ and prove it always exists when $G$ is connected. Specifically, the $P_2$-subgraph eigenvector centrality and $(K_2,F)$-subgraph eigenvector centrality are studied. Some examples show that the ranking of vertices under them differs from the rankings under several classic centralities. Vertices of a regular graph have the same eigenvector centrality scores. But the $(K_2,K_3)$-subgraph eigenvector centrality can distinguish vertices in a given regular graph.