Abstract

The world around us is composed of entities that interact and form relations with each other. This makes graphs an essential data representation and a crucial building-block for machine learning applications; the nodes of the graph correspond to entities and the edges correspond to interactions and relations. The entities and relations may evolve; e.g., new entities may appear, entity properties may change, and new relations may be formed between two entities. This gives rise to dynamic graphs. In applications where dynamic graphs arise, there often exists important information within the evolution of the graph, and modeling and exploiting such information is crucial in achieving high predictive performance. In this chapter, we characterize various categories of dynamic graph modeling problems. Then we describe some of the prominent extensions of graph neural networks to dynamic graphs that have been proposed in the literature. We conclude by reviewing three notable applications of dynamic graph neural networks namely skeleton-based human activity recognition, traffic forecasting, and temporal knowledge graph completion.

Contents

• Introduction
• Background and Notation
• Graph Neural Networks
• Sequence Models
• Encoder-Decoder Framework and Model Training
• Categories of Dynamic Graphs
• Discrete vs. Continues
• Types of Evolution
• Prediction Problems, Interpolation, and Extrapolation
• Modeling Dynamic Graphs with GNNs
• Conversion to Static Graphs
• GNNs for DTDGs
• GNNs for CTDGs
• Applications
• Skeleton-based Human Activity Recognition
• Traffic Forecasting
• Temporal Knowledge Graph Completion
• Summary

Citation