There are 16 repositories under gcn topic.
深度学习入门教程, 优秀文章, Deep Learning Tutorial
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
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
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).
Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
常用的语义分割架构结构综述以及代码复现 华为媒体研究院 图文Caption、OCR识别、图视文多模态理解与生成相关方向工作或实习欢迎咨询 15757172165 https://guanfuchen.github.io/media/hw_zhaopin_20220724_tiny.jpg
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
Learning to Cluster Faces (CVPR 2019, CVPR 2020)
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam,dcn and so on), and tensorrt
A repository of pretty cool datasets that I collected for network science and machine learning research.
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).
Code for CVPR'19 paper Linkage-based Face Clustering via GCN
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
Hierarchical Graph Pooling with Structure Learning
ACL 2019: Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Some TrafficFlowForecasting Solutions(交通流量预测解决方案)
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).