A Visual Analytics Approach to Compare Propagation Models in Social Networks

TL;DR

This paper introduces a visual analytics framework that uses graph rewriting to objectively compare different social influence propagation models in social networks, aiding model selection.

Contribution

It proposes a novel formalism using graph rewriting to describe propagation models and a visual analytics tool for interactive comparison based on simulation traces.

Findings

Framework enables effective comparison of models

Supports interactive manipulation of propagation mechanisms

Highlights differences through measures on simulation traces

Abstract

Numerous propagation models describing social influence in social networks can be found in the literature. This makes the choice of an appropriate model in a given situation difficult. Selecting the most relevant model requires the ability to objectively compare them. This comparison can only be made at the cost of describing models based on a common formalism and yet independent from them. We propose to use graph rewriting to formally describe propagation mechanisms as local transformation rules applied according to a strategy. This approach makes sense when it is supported by a visual analytics framework dedicated to graph rewriting. The paper first presents our methodology to describe some propagation models as a graph rewriting problem. Then, we illustrate how our visual analytics framework allows to interactively manipulate models, and underline their differences based on measures computed on simulation traces.