GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation

TL;DR

GraphGen-Redux is a faster, more efficient recurrent model for labeled graph generation that jointly processes node and edge labels, outperforming previous models in accuracy and resource usage.

Contribution

It introduces a novel preprocessing method and a modified generative model that jointly captures label dependencies, reducing parameters and training time.

Findings

Outperforms GraphGen in diverse datasets

Uses 78% fewer parameters

Requires 50% less training epochs

Abstract

The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several approaches have been proposed in the literature, most of which require to transform the graphs into sequences that encode their structure and labels and to learn the distribution of such sequences through an auto-regressive generative model. Among this family of approaches, we focus on the GraphGen model. The preprocessing phase of GraphGen transforms graphs into unique edge sequences called Depth-First Search (DFS) codes, such that two isomorphic graphs are assigned the same DFS code. Each element of a DFS code is associated with a graph edge: specifically, it is a quintuple comprising one node identifier for each of the two endpoints, their node labels, and the edge label. GraphGen learns to generate such sequences auto-regressively and models the probability of each component of the quintuple independently. While effective, the independence assumption made by the model is too loose to capture the complex label dependencies of real-world graphs precisely. By introducing a novel graph preprocessing approach, we are able to process the labeling information of both nodes and edges jointly. The corresponding model, which we term GraphGen-Redux, improves upon the generative performances of GraphGen in a wide range of datasets of chemical and social graphs. In addition, it uses approximately 78% fewer parameters than the vanilla variant and requires 50% fewer epochs of training on average.