Edge centrality and the total variation of graph distributional signals

TL;DR

This paper explores a new formulation of total variation for graph distributional signals, linking it to edge centrality to offer a novel perspective and potentially easier computation methods.

Contribution

It introduces an alternative formulation of total variation and connects it to edge centrality, providing new insights and computational advantages.

Findings

New formulation of total variation for graph signals

Relation established between total variation and edge centrality

Potential for improved computation of total variation

Abstract

This short note is a supplement to [1], in which the total variation of graph distributional signals is introduced and studied. We introduce a different formulation of total variation and relate it to the notion of edge centrality. The relation provides a different perspective of total variation and may facilitate its computation.