<aside> 🌐

Outdated page — the Temporal Graph Wiki has moved. Read this page on temporalgraph.wiki.

</aside>

Aurora Rossi gave a talk on temporal graph neural networks at the Dagstuhl Seminar 26251 “Temporal Graphs: Structure, Algorithms, Applications” in 06/26:

Slides-26-Aurora_Rossi-TGNN-Dagstuhl26251.pdf

— A short overview of temporal graphs in the area of artificial intelligence, Temporal Neural Networks

Work in progress

Introduction

Temporal graphs are used in advancd artificial intelligence to enable modeling of dynamic systems (interactions change over time). These models are widely used across various domains, such as social networks, recommender systems, and epidemic modeling. They capture temporal dependencies and uncover insights that static graphs cannot provide. The traditional research on computational temporal graph problems is not always directly applicable, but metrics and graph structure theory is used a lot.

Expand on this

Applications in AI

1. Social Network Analysis: Temporal graphs help understand how influence spreads over time, detect communities, and model user interactions.
2. Recommender Systems: They enable dynamic user-item modeling, accounting for temporal behaviors to enhance recommendations.
3. Traffic and Mobility Analysis: Modeling temporal road networks or public transit systems to predict traffic patterns and optimize routing.
4. Health Informatics: Temporal graphs are applied to model patient health trajectories and disease progression in temporal biomedical networks.

“Temporal” Networks used in AI

Discrete-Time Models

Continuous-Time Models

Hybrid Models

Causality-Aware Models

Dynamic Graph Representation Learning Models

Probabilistic Models