Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

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

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

Graph Neural Networks (GNNs) have been successfully applied to many real-world static
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …

Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto
methods to extract spatial-temporal information for temporal graph learning. Interestingly, we …

Despite the prevalence of recent success in learning from static graphs, learning from time-
evolving graphs remains an open challenge. In this work, we design new, more stringent …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …