gelang93

gelang93's Stars

• thunlp/GNNPapers

  Must-read papers on graph neural networks (GNN)

  16k601183k

• pymc-devs/pymc

  Bayesian Modeling and Probabilistic Programming in Python

  Language:Python8.7k2253.3k2k

• tkipf/gcn

  Implementation of Graph Convolutional Networks in TensorFlow

  Language:Python7.1k1571942k

• zhedahht/CodingInterviewChinese2

  《剑指Offer：名企面试官精讲典型编程面试题》第二版源代码

  Language:C++5.3k149822.2k

• farizrahman4u/seq2seq

  Sequence to Sequence Learning with Keras

  Language:Python3.2k158242845

• open-mmlab/mmskeleton

  A OpenMMLAB toolbox for human pose estimation, skeleton-based action recognition, and action synthesis.

  Language:Python2.9k704511k

• Xovee/uestc-course

  电子科技大学课程资料共享平台. Course material sharing platform of UESTC.

  Language:Python2.7k2723409

• weihua916/powerful-gnns

  How Powerful are Graph Neural Networks?

  Language:Python1.2k2623224

• sungyongs/graph-based-nn

  Graph Convolutional Networks (GCNs)

  909540173

• kimiyoung/planetoid

  Semi-supervised learning with graph embeddings

  Language:Python8852220299

• vuptran/graph-representation-learning

  Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)

  Language:Python25311767

• fmonti/mgcnn

  Multi-Graph Convolutional Neural Networks

  Language:Jupyter Notebook25113570

• ZhuangCY/DGCN

  Dual Graph Convolution Networks

  Language:Python934322

• Tixierae/graph_2D_CNN

  Code and data for the paper 'Classifying Graphs as Images with Convolutional Neural Networks' (new title: 'Graph Classification with 2D Convolutional Neural Networks')

  Language:Python775225

• MdAsifKhan/DNGR-Keras

  Learning graph representations using autoencoder

  Language:Python522524

• ShelsonCao/DNGR

  Source Code of DNGR

  Language:MATLAB422423

• fllinares/neural_fingerprints_tf

  A TensorFlow implementation of "Convolutional Networks on Graphs for Learning Molecular Fingerprints".

  Language:Python31329

• ChuXiaokai/CrossMNA

  Language:Python29136

• jdzikowski/iclr2019

  Reproduction of How Powerful are Graph Neural Networks? paper from ICLR 2019

  Language:TeX1022