A generative model for sparse, evolving digraphs

TL;DR

This paper introduces SDG and SEDGE, algorithms for generating sparse, evolving directed graphs that resemble real-world graphs, particularly those representing software, with improved performance over existing methods.

Contribution

The paper presents novel algorithms SDG and SEDGE for generating static and series of sparse, evolving digraphs, addressing the challenge of modeling real graph dynamics.

Findings

SEDGE outperforms existing approaches in generating realistic graph series.

Experiments demonstrate the effectiveness of SDG and SEDGE on software graph data.

The algorithms capture the evolution of real graphs more accurately.

Abstract

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.