Dynamical systems with interacting agents are universal in nature, commonly
modeled by a graph of relationships between their constituents. Recently, various
works have been presented to tackle the problem of inferring those relationships
from the system trajectories via deep neural networks, but most of the studies
assume binary or discrete types of interactions for simplicity. In the real world,
the interaction kernels often involve continuous interaction strengths, which can
not be accurately approximated by discrete relations. In this work, we propose
the relational attentive inference network (RAIN) to infer continuously weighted
interaction graphs without any ground-truth interaction strengths. Our model em
ploys a novel pairwise attention (PA) mechanism to refine the trajectory represen
tations and a graph transformer to extract heterogeneous interaction weights for
each pair of agents. We show that our RAIN model with the PA mechanism ac
curately infers continuous interaction strengths for simulated physical systems in
an unsupervised manner. Further, RAIN with PA successfully predicts trajectories
from motion capture data with an interpretable interaction graph, demonstrating
the virtue of modeling unknown dynamics with continuous weights.