Representation Learning over Dynamic Graphs

Understand

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.