22 citations · 30 across the 11 of their papers we have counts for
12 papers
When Vision Meets Graphs: A Survey on Graph Reasoning and Learning
Xinjian Zhao, Wei Pang, Zhixuan Yu +8
Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the past decade, Graph Neural Networks (GNNs) have dominated graph machine…
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
Chenhan Jin, Shengze Xu, Qingsong Wang +3
Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined pruning method while striving f…
Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
Zhixun Li, Bin Cao, Rui Jiao +9
Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design…
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
Dingshuo Chen, Shuchen Xue, Liuji Chen +5
Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progres…
IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification
Zhixun Li, Dingshuo Chen, Tong Zhao +5
Graph Neural Networks (GNNs) have achieved great success in dealing with non-Euclidean graph-structured data and have been widely deployed in many real-world applications. However,…
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
Guibin Zhang, Haonan Dong, Yuchen Zhang +7
Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset s…
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization
Dingshuo Chen, Zhixun Li, Yuyan Ni +6
With the emergence of various molecular tasks and massive datasets, how to perform efficient training has become an urgent yet under-explored issue in the area. Data pruning (DP),…
GSLB: The Graph Structure Learning Benchmark
Zhixun Li, Liang Wang, Xin Sun +8
Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation g…
Uncovering Neural Scaling Laws in Molecular Representation Learning
Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang +5
Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While th…
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection
Zhixun Li, Dingshuo Chen, Qiang Liu +1
Graph-based fraud detection has heretofore received considerable attention. Owning to the great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for fraud det…
Improving Molecular Pretraining with Complementary Featurizations
Yanqiao Zhu, Dingshuo Chen, Yuanqi Du +3
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry an…
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
Zekai Chen, Dingshuo Chen, Xiao Zhang +2
Many real-world IoT systems, which include a variety of internet-connected sensory devices, produce substantial amounts of multivariate time series data. Meanwhile, vital IoT infra…