Abstract We present the Temporal Graph Benchmark (TGB), a collection of challenging and
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop …

Temporal graphs are widely used to model dynamic systems with time-varying interactions.
In real-world scenarios, the underlying mechanisms of generating future interactions in …

Complex networks pervade various real-world systems, from the natural environment to
human societies. The essence of these networks is in their ability to transition and evolve …

In recent years, Dynamic Graph (DG) representations have been increasingly used for
modeling dynamic systems due to their ability to integrate both topological and temporal …

Many graph representation learning (GRL) problems are dynamic, with millions of edges
added or removed per second. A fundamental workload in this setting is dynamic link …

Graph Neural Networks (GNNs) have been a prominent research area and have been
widely deployed in various high-stakes applications in recent years, leading to a growing …

Dynamic graph modeling is crucial for understanding complex structures in web graphs,
spanning applications in social networks, recommender systems, and more. Most existing …

Dynamic graphs play a crucial role in various real-world applications, such as link prediction
and node classification on social media and e-commerce platforms. Temporal Graph Neural …

Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-
order interactions in complex systems. Representation learning for hypergraphs is essential …