LirongWu/KRD

Stars
11
Rank 1,694,829 (Top 34 %)
Language
Python
License
MIT License
Created over 1 year ago
Updated over 1 year ago

LirongWu/KRD

LirongWu

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Code for ICML 2023 paper "Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs"

awesome-graph-self-supervised-learning

Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

awesome-protein-representation-learning

Awesome Protein Representation Learning

KDGA

Code for NeurIPS 2022 paper "Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks"

MAPE-PPI

Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"

GCML

Code for WACV 2022 paper "Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation"

GraphMixup

Code for ECML-PKDD 2022 paper "GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction"

FF-G2M

Code for AAAI 2023 (Oral) paper "Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework"

RFA-GNN

Code for TNNLS paper "Beyond Homophily and Homogeneity Assumption: Relation-based Frequency Adaptive Graph Neural Networks"

MD-GNN

Code for NCAA paper "Multi-level Disentanglement Graph Neural Network"

PSC-CPI

Code for AAAI 2024 paper "PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction"

DCV

Code for TNNLS paper "Deep Clustering and Visualization for End-to-End High Dimensional Data analysis"

Homophily-Enhanced-Self-supervision

Code for TNNLS paper "Homophily-Enhanced Self-supervision for Graph Structure Learning: Insights and Directions"

L2A

Code for ECML-PKDD 2023 paper "Learning to Augment Graph Structure for both Homophily and Heterophily Graphs"

LirongWu

lirongwu.github.io

TGS

Code for TKDE paper "A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation"