Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

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 …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Graph classification is a widely studied problem and has broad applications. In many real-
world problems, the number of labeled graphs available for training classification models is …

In this work, we propose the first backdoor attack to graph neural networks (GNN).
Specifically, we propose a subgraph based backdoor attack to GNN for graph classification …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

We consider feature representation learning problem of molecular graphs. Graph Neural
Networks have been widely used in feature representation learning of molecular graphs …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Numerous studies have been conducted to investigate the properties of large-scale
temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually …

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology
positions of labeled nodes, which significantly damages the performance of GNNs. What …