Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph
representation learning. Standard GNNs define a local message-passing mechanism which …

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

Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-
structured data. While numerous approaches have been proposed to improve GNNs in …

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 …

Recently, subgraph GNNs have emerged as an important direction for developing
expressive graph neural networks (GNNs). While numerous architectures have been …

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 …

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently,
provably boosting the expressive power of standard (message-passing) GNNs. However …

Abstract Graph Neural Networks (GNN) are inherently limited in their expressive power.
Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the Weisfeiler …

In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Unlike the unified pre-training strategy employed in the language field, the graph field …

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 …