Real-world scenarios demand reasoning about process, more than final outcome
prediction, to discover latent causal chains and better understand complex
systems. It requires the learning algorithms to offer both accurate predictions
and clear interpretations...
We design a set of trajectory reasoning tasks on
graphs with only the source and the destination observed. We present the
attention flow mechanism to explicitly model the reasoning process, leveraging
the relational inductive biases by basing our models on graph networks. We
study the way attention flow can effectively act on the underlying information
flow implemented by message passing. Experiments demonstrate that the attention
flow driven by and interacting with graph networks can provide higher accuracy
in prediction and better interpretation for trajectory reasoning.