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alibaba-custermers-interaction-dataset

deepwalk

DeepWalk - Deep Learning for Graphs

dgfraud

A Deep Graph-based Toolbox for Fraud Detection

dmr

Deep Match to Rank Model for Personalized Click-Through Rate Prediction

euler

A distributed graph deep learning framework.

galileo

Galileo library for large scale graph training by JD

gnn

gnn-recommender-systems

An index of recommendation algorithms that are based on Graph Neural Networks.

gnn-recsys

Graph Neural Networks for Recommender Systems

graph-fraud-detection-papers

A curated list of fraud detection papers using graph information or graph neural networks

group-buying-recommendation

Group-Buying Recommendation for Social E-Commerce

hands-on-neural-networks-with-tensorflow-2.0

Hands-On Neural Networks with TensorFlow 2.0, published by Packt

hierarchical-graph-attention-network

hmgnn

item2vec-neural-item-embedding-for-collaborative-filtering

Python implementation of "Item2Vec: Neural Item Embedding for Collaborative Filtering"

kdd2019-meirec

KDD2019-Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation

mcns

Source code and dataset for KDD 2020 paper "Understanding Negative Sampling in Graph Representation Learning"

neural_graph_collaborative_filtering

Neural Graph Collaborative Filtering, SIGIR2019

ngcf_pytorch_g61

A reproducting of the Neural Graph Collaborative Filtering algorithm in PyTorch

node2vec

Implementation of the node2vec algorithm.

python-louvain

Louvain Community Detection

pytorch_structure2vec

pytorch implementation of structure2vec (https://arxiv.org/abs/1603.05629)

san-for-product-attributes-prediction

sr-gnn

[AAAI 2019] Source code and datasets for "Session-based Recommendation with Graph Neural Networks"

star-gcn

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

wikipedia-data-science

Working with and analyzing Wikipedia Data

word2vecpy

Python implementation of CBOW and skip-gram word vector models, and hierarchical softmax and negative sampling learning algorithms