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23 search results found

Efficient Gnns ⭐ 490

Code and resources on scalable and efficient Graph Neural Networks

Graphcl ⭐ 417

[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training @ KDD 2020

ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction

[AAAI 2023] An official source code for paper Hard Sample Aware Network for Contrastive Deep Graph Clustering.

Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning

Official implementation of RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning

[SIGIR'2023] "DCCF: Disentangled Contrastive Collaborative Filtering"

[WWW'2023] "AutoST: Automated Spatio-Temporal Graph Contrastive Learning"

Graph Papers ⭐ 21

Graph Neural Network, Self-Supervised Learning, Contrastive Learning, RecSys, Transformer Papers Reading Notes.

Source code for "SelfCF: A Simple Framework for Self-supervised Collaborative Filtering". Accepted by ACM TORS.

PyTorch implementation of the AAAI-21 paper "Dual Adversarial Label-aware Graph Neural Networks for Cross-modal Retrieval" and the TPAMI-22 paper "Integrating Multi-Label Contrastive Learning with Dual Adversarial Graph Neural Networks for Cross-Modal Retrieval".

[ICML'2023] "GraphST: Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation"

[KDD'2023] "AdaGCL: Adaptive Graph Contrastive Learning for Recommendation"

Awesome Graph Self Supervised Learning Based Recommendation ⭐ 15

A curated list of awesome graph & self-supervised-learning-based recommendation.

The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

[ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"

Linkdist ⭐ 11

Distillation Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages.

Contrastive pretraining to learn chemical reaction representations (RxnRep) for downstream tasks.

[WWW'2023] "Debiased Contrastive Learning for Sequential Recommendation"

[PAKDD2023] Improving Knowledge Graph Entity Alignment with Graph Augmentation

Brainnn Pretrain ⭐ 7

Implementations for CHIL 2023 paper ''PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis'' and KDD 2022 paper ''Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning''

Gnn_latest_papers ⭐ 6

The latest research papers on graph representation learning[图神经网络最新论文汇总，持续更新中，欢迎关注]

Linkschedulinggnns_supervisionstudy ⭐ 5

A comparative study of different supervision levels (supervised/unsupervised/self-supervised) when training graph neural network models for link scheduling in wireless networks.

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Python Graph Neural Networks (439)

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Python Contrastive Learning (135)

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