  Entity interaction prediction is essential in many important applications
such as chemistry, biology, material science, and medical science. The problem
becomes quite challenging when each entity is represented by a complex
structure, namely structured entity, because two types of graphs are involved:
local graphs for structured entities and a global graph to capture the
interactions between structured entities. We observe that existing works on
structured entity interaction prediction cannot properly exploit the unique
graph of graphs model. In this paper, we propose a Graph of Graphs Neural
Network, namely GoGNN, which extracts the features in both structured entity
graphs and the entity interaction graph in a hierarchical way. We also propose
the dual-attention mechanism that enables the model to preserve the neighbor
importance in both levels of graphs. Extensive experiments on real-world
datasets show that GoGNN outperforms the state-of-the-art methods on two
representative structured entity interaction prediction tasks:
chemical-chemical interaction prediction and drug-drug interaction prediction.
Our code is available at Github.
