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

Molecular Relational Learning (MRL), aiming to understand interactions between molecular
pairs, plays a pivotal role in advancing biochemical research. Recently, the adoption of large …

The density of states (DOS) is a spectral property of crystalline materials, which provides
fundamental insights into various characteristics of the materials. While previous works …

Inductive relation reasoning in knowledge graphs aims at predicting missing triplets
involving unseen entities and/or unseen relations. While subgraph-based methods that …

Inductive relation prediction is an important learning task for knowledge graph reasoning
that aims to infer new facts from existing ones. Previous graph neural networks (GNNs) …

Graphs are ubiquitous for modeling complex relationships between objects across various
fields. Graph neural networks (GNNs) have become a mainstream technique for graph …

Graph neural networks (GNN) are vulnerable to adversarial attacks, which aim to degrade
the performance of GNNs through imperceptible changes on the graph. However, we find …

Temporal Graph Neural Networks (TGNN) have the ability to capture both the graph
topology and dynamic dependencies of interactions within a graph over time. There has …

Drug relational learning, focused on understanding drug-pair relationships within specific
contexts of interest, has emerged as a critical area of investigation for its pivotal role in …