  Reasoning about graphs evolving over time is a challenging concept in many
domains, such as bioinformatics, physics, and social networks. We consider a
common case in which edges can be short term interactions (e.g., messaging) or
long term structural connections (e.g., friendship). In practice, long term
edges are often specified by humans. Human-specified edges can be both
expensive to produce and suboptimal for the downstream task. To alleviate these
issues, we propose a model based on temporal point processes and variational
autoencoders that learns to infer temporal attention between nodes by observing
node communication. As temporal attention drives between-node feature
propagation, using the dynamics of node interactions to learn this key
component provides more flexibility while simultaneously avoiding issues
associated with human-specified edges. We also propose a bilinear
transformation layer for pairs of node features instead of concatenation,
typically used in prior work, and demonstrate its superior performance in all
cases. In experiments on two datasets in the dynamic link prediction task, our
model often outperforms the baseline model that requires a human-specified
graph. Moreover, our learned attention is semantically interpretable and infers
connections similar to actual graphs.
