Zhiqiang ZHONG (zhiqiangzhongddu)

zhiqiangzhongddu

Geek Repo

Company:Aarhus University

Location:Denmark

Home Page:https://zhiqiangzhongddu.github.io/

Twitter:@zhong_zhiqiang

Github PK Tool:Github PK Tool

Zhiqiang ZHONG's repositories

Data-Science-Interview-Questions-and-Answers-General-

Data Science Questions and Answers (General) for beginner

Awesome-Knowledge-augmented-GML-for-Drug-Discovery

A collection of resources related with Knowledge-augmented Graph Machine Learning for Drug Discovery.

KDD2023_KaGML_DrugDiscovery_Tutorial

Materials for KDD2023 tutorial: Knowledge-augmented Graph Machine Learning for Drug Discovery: from Precision to Interpretability

HC-GNN

[DMKD-ECMLPKDD] Hierarchical Message-Passing Graph Neural Networks (https://arxiv.org/abs/2009.03717)

SELENE

[TMLR] Unsupervised Network Embedding Beyond Homophily (https://arxiv.org/abs/2203.10866) Resources

PM-HGNN

[DMKD-ECMLPKDD] Personalised Meta-path Generation for Heterogeneous Graph Neural Networks (https://arxiv.org/abs/2010.13735)

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AdamGNN

[TKDE] Multi-grained Semantics-aware Graph Neural Networks (https://arxiv.org/abs/2010.00238)

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TMLR-CLP

[TMLR] Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation (https://arxiv.org/abs/2205.09389)

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NeuLP

[WISE20] NeuLP: An End-to-end Deep-learning Model for Link Prediction (https://dl.acm.org/doi/abs/10.1007/978-3-030-62005-9_8)

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GearNet

GearNet and Geometric Pretraining Methods for Protein Structure Representation Learning, ICLR'2023 (https://arxiv.org/abs/2203.06125)

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GNNPapers

Must-read papers on graph neural networks (GNN)

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H2GCN

Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)

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Learning-Sleep-Quality-from-Daily-Logs

Learning Sleep Quality from Daily Logs in Tensorflow.

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NodeFormer

The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"

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PEER_Benchmark

PEER Benchmark, appear at NeurIPS 2022 Dataset and Benchmark Track (https://arxiv.org/abs/2206.02096)

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PosthocGnnExplainerRobust

[LOG] On the Robustness of Post-hoc GNN Explainers to Label Noise (https://arxiv.org/pdf/2309.01706)

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pytorch_geometric

Graph Neural Network Library for PyTorch

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Pytorch_tutorial

tutorial docs for Pytorch

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