Information Flow in Interaction Networks

TL;DR

This paper introduces a simple mathematical framework using random walks and potential functions to model context-specific information flow in interaction networks, accounting for aging of information.

Contribution

It presents a novel formalism for modeling information propagation in interaction networks that incorporates context and dissipation, expanding beyond traditional graph-based methods.

Findings

Demonstrated utility on yeast protein interaction networks

Applied to histone acetyltransferases in transcription control

Showed how potential functions direct information flow

Abstract

Interaction networks, consisting of agents linked by their interactions, are ubiquitous across many disciplines of modern science. Many methods of analysis of interaction networks have been proposed, mainly concentrating on node degree distribution or aiming to discover clusters of agents that are very strongly connected between themselves. These methods are principally based on graph-theory or machine learning. We present a mathematically simple formalism for modelling context-specific information propagation in interaction networks based on random walks. The context is provided by selection of sources and destinations of information and by use of potential functions that direct the flow towards the destinations. We also use the concept of dissipation to model the aging of information as it diffuses from its source. Using examples from yeast protein-protein interaction networks and some of the histone acetyltransferases involved in control of transcription, we demonstrate the utility of the concepts and the mathematical constructs introduced in this paper.