Hierarchical community-based graph generation model for improving structural diversity

Abstract

Graph generation remains a challenging task due to the high dimensionality of graphs and the complex dependencies among their edges. Existing models often struggle to produce structurally diverse graphs. To address this limitation, we propose a novel generative framework specifically designed to capture structural diversity in graph generation. Our approach follows a sequential process: initially, a community detection algorithm partitions the input graph into distinct communities. Each community is then generated independently using deep generative models, while a dedicated module concurrently learns the interconnections between communities. To scale to graphs with a larger number of communities, we extend our approach into a hierarchical generative model. The proposed framework not only improves generation accuracy but also significantly reduces generation time for large-scale graphs. Moreover, it enables the application of prior methods that were previously incapable of handling such graphs. To highlight the shortcomings of existing approaches, we conduct experiments on a synthetic dataset comprising diverse graph structures. The results demonstrate substantial improvements in standard evaluation metrics as well as in the quality of the generated graphs.