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Name: GNN
Type: Organization
Bio: Graph Neural Networks, Graph Theory
Location: ECUT
Name: GNN
Type: Organization
Bio: Graph Neural Networks, Graph Theory
Location: ECUT
Multiscale Dynamic Graph Convolutional Network for hyperspectral image classification
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
MWP for superpixel GNN classification
MNIST classification by using GCN
The reference implementation of "Multi-scale Attributed Node Embedding".
Spectral graph clustering of a dataset of mushrooms
Papers on Graph neural network(GNN)
Implementation of 'Large-Scale Multi-View Spectral Clustering via Bipartite Graph'
Graph neural net for neutron captures
Object detection using semantic segmentation
Benchmark datasets, data loaders, and evaluators for graph machine learning
OhmNet: Representation learning in multi-layer graphs
Graph-based clustering using the inverse power method for nonlinear eigenproblems
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
Experiments to use graphs with spacial and similarity relationships of image features for object detection
Generating PGM Explanation for GNN predictions
Semi-supervised learning with graph embeddings
How Powerful are Graph Neural Networks?
Strategies for Pre-training Graph Neural Networks
Spectral Clustering based on the graph p-Laplacian
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
Graph Convolutional Networks in PyTorch
Graphs clustering using kernel measures and estimators. Kernel KMeans, Spectral Clustering, Kernel Ward etc.
Graph signal processing tutorial, presented at the GraphSiP summer school.
Code for "Heterogeneous Graph Transformer" (WWW'20), which is based on pytorch_geometric
Integration of networks to enhance semi-supervised learning algorithms over graphs.
Generate embeddings from large-scale graph-structured data.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.