A Generative Model for Sparse, Evolving Digraphs
Generating graphs that are similar to real ones is an open problem, while the similarity notion is quite elusive and hard to formalize. In this paper, we focus on sparse digraphs and propose SDG, an algorithm that aims at generating graphs similar to real ones. Since real graphs are evolving and this evolution is important to study in order to understand the underlying dynamical system, we tackle the problem of generating series of graphs. We propose SEDGE, an algorithm meant to generate series of graphs similar to a real series. SEDGE is an extension of SDG. We consider graphs that are representations of software programs and show experimentally that our approach outperforms other existing approaches. Experiments show the performance of both algorithms.
This work was partially supported by CPER Nord-Pas de Calais/FEDER DATA Advanced data science and technologies 2015–2020, and the French Ministry of Higher Education and Research. We also wish to acknowledge the continual support of Inria, and the stimulating environment provided by the SequeL Inria project-team.
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