Challenges of Generating Structurally Diverse Graphs

Fedor Velikonivtsev, Mikhail Mironov, Liudmila Prokhorenkova

Keywords: graphs, diverse graphs, diversity, graph generation, graph distance

TL;DR: We formulate the problem of generating structurally diverse graphs, define what it means for a set of graphs to be diverse, and propose various approaches to address this problem.

Abstract: We address the problem of generating graphs that are structurally diverse. First, we discuss how to define diversity for a set of graphs, why this task is non-trivial, and how one can choose a proper diversity measure. Then, for a given diversity measure, we propose and compare several algorithms optimizing it: we consider approaches based on standard random graph models, local graph optimization, genetic algorithms, and neural generative models. We show that it is possible to significantly improve diversity over basic random graph generators and generate graphs with diverse structural characteristics.

Submission Type: Extended abstract (max 4 main pages).

Submission Number: 51