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).
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
Learning to Cluster Faces (CVPR 2019, CVPR 2020)
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).