Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions

The interactions between structured entities play important roles in a wide range of applications such as chemistry, material science, biology, and medical science. Recently, graph-based methods have been exploited to effectively predict the interactions among structured entities. However, these methods usually only focus on structural information of the entities and are incapable of fully utilizing the interaction information between the entities. In this paper, we propose a Denoising Variational Graph of Graphs Auto-encoder (DVGGA) which follows the graph of graphs framework to capture both structural information in structured entities and interaction information among structured entities. With denoising criterion, DVGGA is able to capture the information from the useful structures of the local graph and address the overfitting issue caused by redundant substructures. Extensive experiments conducted on real-world datasets show that DVGGA outperforms the state-of-the-art structured entity interaction prediction methods.