82 citations · 305 across the 22 of their papers we have counts for
9 papers · 1 filter
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Qiaoyu Tan, Xin Zhang, Ninghao Liu +5
Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply G…
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
Huiyuan Chen, Xiaoting Li, Kaixiong Zhou +4
There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability fo…
Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising
Zhimeng Jiang, Kaixiong Zhou, Mi Zhang +3
Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side platform (DS…
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Cameron Diao, Kaixiong Zhou, Zirui Liu +2
Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their struct…
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen +4
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advan…
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
Zirui Liu, Shengyuan Chen, Kaixiong Zhou +3
The training of graph neural networks (GNNs) is extremely time consuming because sparse graph-based operations are hard to be accelerated by hardware. Prior art explores trading of…