  Many complex natural and cultural phenomena are well modelled by systems of
simple interactions between particles. A number of architectures have been
developed to articulate this kind of structure, both implicitly and explicitly.
We consider an unsupervised explicit model, the NRI model, and make a series of
representational adaptations and physically motivated changes. Most notably we
factorise the inferred latent interaction graph into a multiplex graph,
allowing each layer to encode for a different interaction-type. This fNRI model
is smaller in size and significantly outperforms the original in both edge and
trajectory prediction, establishing a new state-of-the-art. We also present a
simplified variant of our model, which demonstrates the NRI's formulation as a
variational auto-encoder is not necessary for good performance, and make an
adaptation to the NRI's training routine, significantly improving its ability
to model complex physical dynamical systems.
