  Graphs arise naturally in many real-world applications including social
networks, recommender systems, ontologies, biology, and computational finance.
Traditionally, machine learning models for graphs have been mostly designed for
static graphs. However, many applications involve evolving graphs. This
introduces important challenges for learning and inference since nodes,
attributes, and edges change over time. In this survey, we review the recent
advances in representation learning for dynamic graphs, including dynamic
knowledge graphs. We describe existing models from an encoder-decoder
perspective, categorize these encoders and decoders based on the techniques
they employ, and analyze the approaches in each category. We also review
several prominent applications and widely used datasets and highlight
directions for future research.
