X He, B Hooi, T Laurent, A Perold… - International …, 2023 - proceedings.mlr.press
Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which …
A Behrouz, F Hashemi - Proceedings of the 30th ACM SIGKDD …, 2024 - dl.acm.org
Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating …
Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper …
Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to identify potential drug candidates. Traditional methods, grounded in physicochemical …
Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric …
Z Zhang, Z Lu, H Zhongkai… - Advances in Neural …, 2023 - proceedings.neurips.cc
The design of\emph {de novo} functional proteins that bind with specific ligand molecules is crucial in various domains like therapeutics and bio-engineering. One vital yet challenging …
Abstract Graph Transformers (GTs) have proved their advantage in graph-level tasks. However, existing GTs still perform unsatisfactorily on the node classification task due to 1) …
Z Zhang, Q Liu, Z Wang, Z Lu… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned …
S Luan, C Hua, Q Lu, L Ma, L Wu, X Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of …