[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
Generative Pre-trained Graph Eulerian Transformer [ICML2025]
Bags of Tricks in OGB (node classification) with GCNs.
Top1 Solution on OGB Challenge (Graph Property Prediction on HIV dataset)
A DGL implementation of "DeeperGCN: All You Need to Train Deeper GCNs".
A DGL implementation of "Combining Label Propagation and Simple Models Out-performs Graph Neural Networks" (ICLR 2021).
S3GRL is a scalable SGRL method for faster link prediction using efficient precomputations.
beeHIVe: AI-powered platform for HIV drug discovery and prediction.
GNNs for protein graph classification on OGBG-PPA with GCN, GIN and baseline models.
Exploration of vanilla GIN, GIN with edge heuristics in prediction head, Heuristics weighted Edge-GNN for link prediction in ogbl-ddi dataset
AA-DC is a structural correction framework for link prediction. It addresses two systematic biases that arise when relational feasibility is evaluated under coarse recognition mappings, such as Adamic–Adar (AA).
Baseline Evaluation for the project "Acceleration of Neural Network Training with Microsoft DeepSpeed"
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