There are 14 repositories under gcn topic.
深度学习入门教程, 优秀文章, Deep Learning Tutorial
A distributed graph deep learning framework.
该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记
StellarGraph - Machine Learning on Graphs
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Next RecSys Library
resources for graph convolutional networks （图卷积神经网络相关资源）
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
Learning to Cluster Faces (CVPR 2019, CVPR 2020)
Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
A repository of pretty cool datasets that I collected for network science and machine learning research.
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]
An index of recommendation algorithms that are based on Graph Neural Networks.
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
Code for CVPR'19 paper Linkage-based Face Clustering via GCN
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).
Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
ACL 2019: Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)
Hierarchical Graph Pooling with Structure Learning
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
Multi-turn dialogue baselines written in PyTorch
Some TrafficFlowForecasting Solutions(交通流量预测解决方案)