Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

The adaptive processing of graph data is a long-standing research topic that has been lately
consolidated as a theme of major interest in the deep learning community. The snap …

Graph Neural Networks have attracted increasing attention in recent years. However,
existing GNN frameworks are deployed based upon simple graphs, which limits their …

Abstract We present Graph Neural Diffusion (GRAND) that approaches deep learning on
graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as …

Cellular sheaves equip graphs with a``geometrical''structure by assigning vector spaces and
linear maps to nodes and edges. Graph Neural Networks (GNNs) implicitly assume a graph …

Graph transformers have emerged as a promising architecture for a variety of graph learning
and representation tasks. Despite their successes, though, it remains challenging to scale …

We investigate the representation power of graph neural networks in the semi-supervised
node classification task under heterophily or low homophily, ie, in networks where …

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 …

Node classification is a classical graph representation learning task on which Graph Neural
Networks (GNNs) have recently achieved strong results. However, it is often believed that …

Message-passing neural networks (MPNNs) have been successfully applied to
representation learning on graphs in a variety of real-world applications. However, two …