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

Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link
Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph
domain, aiming to develop graph models trained on extensive and diverse data to enhance …

Temporal networks have been widely used to model real-world complex systems such as
financial systems and e-commerce systems. In a temporal network, the joint neighborhood of …

Link prediction, a fundamental task on graphs, has proven indispensable in various
applications, eg, friend recommendation, protein analysis, and drug interaction prediction …

Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs
due to their impressive capability of capturing nonlinear relations in graph-structured data …

A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised
learning (SSL), which aims to derive useful node representations without labeled data …

Abstract Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto}
standard in graph representation learning. However, when it comes to link prediction, they …

Link prediction is a fundamental task in graph learning, inherently shaped by the topology of
the graph. While traditional heuristics are grounded in graph topology, they encounter …