The ability to detect and count certain substructures in graphs is important for solving many
tasks on graph-structured data, especially in the contexts of computational chemistry and …

Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural
Networks (GNNs). The limited representational power of MPNNs inspires the study of …

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 …

Despite that going deep has proven successful in many neural architectures, the existing
graph transformers are relatively shallow. In this work, we explore whether more layers are …

Weighted single large graphs are often used to simulate complex systems, and thus mining
frequent subgraphs in a weighted large graph is an important issue that has attracted the …

Using graph neural networks (GNNs) to approximate specific functions such as counting
graph substructures is a recent trend in graph learning. Among these works, a popular way …

Graph neural networks (GNNs) are recently proposed neural network structures for the
processing of graph-structured data. Due to their employed neighbor aggregation strategy …

We perform the first adversarial robustness study into Graph Neural Networks (GNNs) that
are provably more powerful than traditional Message Passing Neural Networks (MPNNs). In …

Recently, there has been an emerging trend to integrate persistent homology (PH) into
graph neural networks (GNNs) to enrich expressive power. However, naively plugging PH …

Frequent subgraph mining (FSM) is a crucial research area with diverse applications.
However, traditional FSM treats all subgraphs as equally important. In practical applications …