  How can we effectively encode evolving information over dynamic graphs into
low-dimensional representations? In this paper, we propose DyRep, an inductive
deep representation learning framework that learns a set of functions to
efficiently produce low-dimensional node embeddings that evolves over time. The
learned embeddings drive the dynamics of two key processes namely,
communication and association between nodes in dynamic graphs. These processes
exhibit complex nonlinear dynamics that evolve at different time scales and
subsequently contribute to the update of node embeddings. We employ a
time-scale dependent multivariate point process model to capture these
dynamics. We devise an efficient unsupervised learning procedure and
demonstrate that our approach significantly outperforms representative
baselines on two real-world datasets for the problem of dynamic link prediction
and event time prediction.
