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.
@article{arxiv.1504.02612,
title = {A Visual Analytics Approach to Compare Propagation Models in Social Networks},
author = {Jason Vallet and Hélène Kirchner and Bruno Pinaud and Guy Melançon},
journal= {arXiv preprint arXiv:1504.02612},
year = {2015}
}