Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

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 …

Masked graph modeling excels in the self-supervised representation learning of molecular
graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three …

Graph neural networks (GNNs) are commonly described as being permutation equivariant
with respect to node relabeling in the graph. This symmetry of GNNs is often compared to …

Recently, many works studied the expressive power of graph neural networks (GNNs) by
linking it to the $1 $-dimensional Weisfeiler-Leman algorithm ($1\text {-}\mathsf {WL} $) …

Relational pooling is a framework for building more expressive and permutation-invariant
graph neural networks. However, there is limited understanding of the exact enhancement in …

In this paper, we propose the first framework that enables solving graph learning tasks of all
levels (node, edge and graph) and all types (generation, regression and classification) using …

The ability of graph neural networks (GNNs) to count certain graph substructures, especially
cycles, is important for the success of GNNs on a wide range of tasks. It has been recently …

Subgraph GNNs are provably expressive neural architectures that learn graph
representations from sets of subgraphs. Unfortunately, their applicability is hampered by the …