Beyond pairwise network similarity: exploring Mediation and Suppression between networks

TL;DR

This paper extends network similarity measures to triplets of networks, introducing a set-theoretic approach to identify mediation and suppression effects, validated on synthetic and real-world networks.

Contribution

It introduces a novel framework for analyzing triplet network relationships, capturing mediation and suppression effects beyond pairwise measures.

Findings

Unveiled mediation effects in social networks.

Detected suppression effects in brain networks.

Validated approach with synthetic and real data.

Abstract

Network similarity measures quantify how and when two networks are symmetrically related, including measures of statistical association such as pairwise distance or other correlation measures between networks or between the layers of a multiplex network, but neither can directly unveil whether there are hidden confounding network factors nor can they estimate when such correlation is underpinned by a causal relation. In this work we extend this pairwise conceptual framework to triplets of networks and quantify how and when a network is related to a second network directly or via the indirect mediation or interaction with a third network. Accordingly, we develop a simple and intuitive set-theoretic approach to quantify mediation and suppression between networks. We validate our theory with synthetic models and further apply it to triplets of real-world networks, unveiling mediation and suppression effects which emerge when considering different modes of interaction in online social networks and different routes of information processing in the brain.