Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

Graph out-of-distribution (OOD) generalization remains a major challenge in graph learning
since graph neural networks (GNNs) often suffer from severe performance degradation …

The remarkable success of Large Language Models (LLMs) across diverse tasks has driven
the research community to extend their capabilities to molecular applications. However …

Neuro-symbolic neural networks have been extensively studied to integrate symbolic
operations with neural networks, thereby improving systematic generalization. Specifically …

Abstract Graph Neural Networks (GNNs) have been proven to easily overfit spurious
subgraphs in the available data, which reduces their trustworthiness in high-stakes real …

Graph machine learning (GML) has been successfully applied across a wide range of tasks.
Nonetheless, GML faces significant challenges in generalizing over out‐of‐distribution …

Graph neural networks (GNNs) have revolutionized molecule representation learning by
modeling molecules as graphs, with atoms represented as nodes and chemical bonds as …

Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for
capturing complex dependencies within diverse graph-structured data. Despite their …

Recent years have witnessed rapid advances in graph representation learning, with the
continuous embedding approach emerging as the dominant paradigm. However, such …

Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph
learning, as real-world graph data often exhibit diverse and shifting environments that …