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 …

Learning representations on large-sized graphs is a long-standing challenge due to the inter-
dependence nature involved in massive data points. Transformers, as an emerging class of …

Real-world data generation often involves complex inter-dependencies among instances,
violating the IID-data hypothesis of standard learning paradigms and posing a challenge for …

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD
algorithms either rely on restricted assumptions or fail to exploit environment information in …

Graph neural networks (GNNs) are widely used in machine learning for graph-structured
data. Even though GNNs have achieved remarkable success in real-world applications …

With the increasing demands of training graph neural networks (GNNs) on large-scale
graphs, graph data condensation has emerged as a critical technique to relieve the storage …

Graph neural networks (GNNs) are effective machine learning models for many graph-
related applications. Despite their empirical success, many research efforts focus on the …

With the prosperity of contrastive learning for visual representation learning (VCL), it is also
adapted to the graph domain and yields promising performance. However, through a …

Abstract E (n)-Equivariant Graph Neural Networks (EGNNs) are among the most widely
used and successful models for representation learning on geometric graphs (eg, 3D …

Graph structure learning is a well-established problem that aims at optimizing graph
structures adaptive to specific graph datasets to help message passing neural networks (ie …