Hierarchical recurrent neural networks for graph generation

Graph generation is widely used in various fields, such as social science, chemistry, and physics. Although the deep graph generative models have achieved considerable success in recent years, some problems still need to be addressed. First, some models learn only the structural information and cannot capture the semantic information. Second, many existing models cannot be applied to large-scale datasets owing to a complex training process and a large number of parameters to be learned. Third, most of the existing models require domain-specific rules to be set up, resulting in poor generalization. To address the aforementioned problems, we propose a domain-agnostic model with hierarchical recurrent neural networks, named GHRNN, which learns the distribution of graph data for generating new graphs. First, we utilize the minimum depth-first-search code to convert a graph into a unique edge sequence that naturally generalizes to various graphs. Then, we adopt the edge-level recurrent neural network (RNN) module to learn the global structure of a graph and the element-level RNN module to obtain semantic information, such as node labels, edge labels, and the timestamp of nodes. The element-level RNN module with a fixed and small number of time steps in GHRNN can efficiently scale to large datasets. Finally, we generate a large number of graphs and evaluate the effectiveness of our model by using 11 different metrics on diverse datasets ranging from molecules to citation networks. Extensive experimental results indicate that GHRNN can capture complex dependencies and generate the most realistic and diverse graphs, in contrast to several state-of-the-art baselines.