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

Link prediction attempts to predict whether an unseen edge exists based on only a portion of
the graph. A flurry of methods has been created in recent years that attempt to make use of …

Foundation models in language and vision have the ability to run inference on any textual
and visual inputs thanks to the transferable representations such as a vocabulary of tokens …

Abstract 2D image understanding is a complex problem within computer vision, but it holds
the key to providing human-level scene comprehension. It goes further than identifying the …

Despite its outstanding performance in various graph tasks, vanilla Message Passing Neural
Network (MPNN) usually fails in link prediction tasks, as it only uses representations of two …

Exploring microbial stress responses to drugs is crucial for the advancement of new
therapeutic methods. While current artificial intelligence methodologies have expedited our …

Recent work has shown the utility of developing machine learning models that respect the
structure and symmetries of eigenvectors. These works promote sign invariance, since for …

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

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 …

Effective brain representation learning is a key step toward the understanding of cognitive
processes and diagnosis of neurological diseases/disorders. Existing studies have focused …