Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

This work introduces DiGress, a discrete denoising diffusion model for generating graphs
with categorical node and edge attributes. Our model utilizes a discrete diffusion process …

The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …

Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …

Abstract Out-of-distribution (OOD) generalization is an important issue for Graph Neural
Networks (GNNs). Recent works employ different graph editions to generate augmented …

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have
facilitated various applications from drug discovery to recommender systems. Nevertheless …

Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of
neural networks, effectively demonstrating they can learn to execute classical algorithms on …

Graph data augmentation has shown superiority in enhancing generalizability and
robustness of GNNs in graph-level classifications. However, existing methods primarily …