We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer
with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph …

Abstract Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …

Abstract Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired
by the structure and function of the human brain. In the context of supply chain management …

The most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message
passing---aggregating information from 1-hop neighbors repeatedly. However, the …

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 …

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 …

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 …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …