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2021-maxp-dgl-graphml-competition-intermediate-solution-

500lines

500 Lines or Less

adaptive-graph-convolutional-network

AGCN - Spectral ChevNet built on Adaptive, trainable graphs

appnp

A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).

awesome-deep-learning-papers-for-search-recommendation-advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer, Reinforcement Learning, Self-supervised Learning and so on.

awesome-gnn-research

My future research

balanced-dataparallel

这里是改进了pytorch的DataParallel, 用来平衡第一个GPU的显存使用量

bine

BiNE: Bipartite Network Embedding

correctandsmooth

[ICLR 2021] Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (https://arxiv.org/abs/2010.13993)

deepctr

Easy-to-use,Modular and Extendible package of deep-learning based CTR models for search and recommendation.

deeprl-tutorials

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

drl4recsys

Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender systems

easy-rl

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

fastgcn

The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""

fastgcn_pytorch

an implementation of FastGCN with pytorch

gamlp

Code of GAMLP for Open Graph Benchmark

gatne

Source code and dataset for KDD 2019 paper "Representation Learning for Attributed Multiplex Heterogeneous Network"

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gcnii

PyTorch implementation of "Simple and Deep Graph Convolutional Networks"

google-research

Google Research

grand

Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"

graph_transformer_networks

Graph Transformer Networks (Authors' PyTorch implementation for the NeurIPS 19 paper)

graphembedding

Implementation and experiments of graph embedding algorithms.

graphmix

Code for reproducing results in GraphMix paper

graphormer

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

graphsage-pytorch

A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.

graphsaint

[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).

grover

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data

gym

A toolkit for developing and comparing reinforcement learning algorithms.