  Many complex processes can be viewed as dynamical systems of interacting
agents. In many cases, only the state sequences of individual agents are
observed, while the interacting relations and the dynamical rules are unknown.
The neural relational inference (NRI) model adopts graph neural networks that
pass messages over a latent graph to jointly learn the relations and the
dynamics based on the observed data. However, NRI infers the relations
independently and suffers from error accumulation in multi-step prediction at
dynamics learning procedure. Besides, relation reconstruction without prior
knowledge becomes more difficult in more complex systems. This paper introduces
efficient message passing mechanisms to the graph neural networks with
structural prior knowledge to address these problems. A relation interaction
mechanism is proposed to capture the coexistence of all relations, and a
spatio-temporal message passing mechanism is proposed to use historical
information to alleviate error accumulation. Additionally, the structural prior
knowledge, symmetry as a special case, is introduced for better relation
prediction in more complex systems. The experimental results on simulated
physics systems show that the proposed method outperforms existing
state-of-the-art methods.
