Centrality measures for graphons: Accounting for uncertainty in networks

TL;DR

This paper introduces a robust, graphon-based framework for defining and analyzing node centrality measures in large, uncertain networks, providing theoretical guarantees and uncertainty bounds.

Contribution

It formalizes centrality measures for graphons, connecting them to classical measures, and establishes concentration inequalities for their limits and uncertainty quantification.

Findings

Graphon centrality measures are limits of classical measures on large graphs.

Concentration inequalities provide bounds on the deviation between graphon and graph measures.

The approach offers robustness to stochastic variations in network data.

Abstract

As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally and conceptually challenging. In particular, node centrality measures rely on the assumption that the graph is perfectly known -- a premise not necessarily fulfilled for large, uncertain networks. Accordingly, centrality measures may fail to faithfully extract the importance of nodes in the presence of uncertainty. To mitigate these problems, we suggest a statistical approach based on graphon theory: we introduce formal definitions of centrality measures for graphons and establish their connections to classical graph centrality measures. A key advantage of this approach is that centrality measures defined at the modeling level of graphons are inherently robust to stochastic variations of specific graph realizations. Using the theory of linear integral operators, we define degree, eigenvector, Katz and PageRank centrality functions for graphons and establish concentration inequalities demonstrating that graphon centrality functions arise naturally as limits of their counterparts defined on sequences of graphs of increasing size. The same concentration inequalities also provide high-probability bounds between the graphon centrality functions and the centrality measures on any sampled graph, thereby establishing a measure of uncertainty of the measured centrality score. The same concentration inequalities also provide high-probability bounds between the graphon centrality functions and the centrality measures on any sampled graph, thereby establishing a measure of uncertainty of the measured centrality score.