Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Recently, transformer architectures for graphs emerged as an alternative to established
techniques for machine learning with graphs, such as (message-passing) graph neural …

Node-level random walk has been widely used to improve Graph Neural Networks.
However, there is limited attention to random walk on edge and, more generally, on $ k …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …

The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of
graph learning tasks strongly dependent on long-range interaction between vertices …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks (GNNs), stands out for its capability to capture intricate relationships within …

Graph neural networks based on iterative one-hop message passing have been shown to
struggle in harnessing the information from distant nodes effectively. Conversely, graph …

Graph neural networks are popular architectures for graph machine learning, based on
iterative computation of node representations of an input graph through a series of invariant …

Graph transformers (GTs) have emerged as a promising architecture that is theoretically
more expressive than message-passing graph neural networks (GNNs). However, typical …

Graph transformers have recently received significant attention in graph learning, partly due
to their ability to capture more global interaction via self-attention. Nevertheless, while higher …