Neural Relational Inference with Node-Specific Information

Inferring interactions among entities is an important problem in studying dynamicalsystems, which greatly impacts the performance of downstream tasks, such asprediction. In this paper, we tackle this problem in a setting where each entitycan potentially have a set of individualized information that other entities cannothave access to. Specifically, we represent the system using a graph in which theindividualized information become node-specific information (NSI). We build ourmodel in the framework of Neural Relation Inference (NRI), where the interactionamong entities are interpretably uncovered using variational inference. We adoptNRI model to incorporate the individualized information by introducingprivatenodesin the graph that represent NSI. Such representation enables us to uncovermore accurate relations among the agents and therefore leads to better performanceon the downstream tasks. Our experiment results over real-world datasets validatethe merit of our proposed algorithm.