Towards Addressing Frontiers in Graph Generation

Abstract

This doctoral dissertation establishes and addresses frontiers in graph generation. I first apply a Graph Neural Network (GNN) model on social network data, a new domain, to establish what frontiers exist for graph generators. I establish that GNN models are currently limited in the diversity of feature sets that they can produce, the variety of graph structure types they can generate, and highly limited in the size of generated graphs. Further, I find that the quality metrics available for graph generation are aggregate-based and un-expressive. To address the issue of scale I propose Hierarchical Generation of Graphs (HiGGs), a framework for producing graphs orders of magnitude larger than is possible with a single model. As a step towards more expressive metrics I develop Topology only Pre-training (ToP), a pre-training framework for graph models that is capable of representing multiple domains of graphs simultaneously, without relying on tertiary models in downstream applications. The next stage of research will adapt ToP as a model based metric for graph generators.