35 citations · 68 across the 7 of their papers we have counts for
8 papers
ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery
Xiaobao Huang, Yihong Ma, Anjali Gurajapu +4
Reaction virtual screening and discovery are fundamental challenges in chemistry and materials science, where traditional graph neural networks (GNNs) struggle to model multi-react…
Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective
Yihong Ma, Xiaobao Huang, Bozhao Nan +4
The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction. Accurate yield predictio…
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
Yihong Ma, Ning Yan, Jiayu Li +2
Graphs have emerged as a natural choice to represent and analyze the intricate patterns and rich information of the Web, enabling applications such as online page classification an…
Class-Imbalanced Learning on Graphs: A Survey
Yihong Ma, Yijun Tian, Nuno Moniz +1
The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor p…
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers
Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno +2
CyberPhysical systems (CPS) must be closely monitored to identify and potentially mitigate emergent problems that arise during their routine operations. However, the multivariate t…
Graph-based Molecular Representation Learning
Zhichun Guo, Kehan Guo, Bozhao Nan +8
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vector…
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks
Yijun Tian, Chuxu Zhang, Zhichun Guo +3
Learning effective recipe representations is essential in food studies. Unlike what has been developed for image-based recipe retrieval or learning structural text embeddings, the…
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
Daheng Wang, Zhihan Zhang, Yihong Ma +4
Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the no…