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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
Implementation of MoNet (mixture model CNN) and GAT (Graph Attention Network) tested on MNIST and Cora datasets using Tensorflow 2.0.
links to conference publications in graph-based deep learning
This project explores the different techniques (both scalable and non scalable) for Graph based semi supervised learning. Recent techniques such as ITML and LMNN along with a few others are empirically evaluated on the 20 newsgroups dataset.
Graph Capsule Convolutional Neural Networks
unsupervised clustering, generative model, mixed membership stochastic block model, kmeans, spectral clustering, point cloud data
Graph clustering project using Markov clustering algorithm, K-medoid algorithm, Spectral algorithm with GUI PyQt5
MXNet implementation of Graph Convolutional Neural Networks
Source code for the Neo4j Graph Data Science library of graph algorithms.
A Tensorflow 2.0 implementation of Graph Isomorphism Networks.
A PyTorch implementation of various Graph Neural Networks (GNNs) for graph classification
Sample Code for Graph Partition Neural Networks
Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)
Graph Signal Processing and Spectral Clustering
python implementation of the k-eigenvector spectral graph clustering algorithm
Pytorch implementation of Graph U-Nets (ICML19)
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
TensorFlow 2 implementation of state-of-the-arts graph adversarial attack and defense models (methods).
Code for "GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction"
Examples for graph based CNNs with keras
Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image
A collection of graph data used for semi-supervised node classification.
Graph (network) embeddings evaluation framework via classification, gram martix construction for links prediction
A gallery of state-of-the-arts deep learning graph models. Implemented with Tensorflow 2.x.
A tensorflow implementation of GraphGAN (Graph Representation Learning with Generative Adversarial Nets)
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.