MXNet implementation of Graph Convolutional Neural Networks
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Updated
Oct 8, 2018 - Python
MXNet implementation of Graph Convolutional Neural Networks
Daily reading group on graphs at KEG
A tensorflow implementation of GraphGAN (Graph Representation Learning with Generative Adversarial Nets)
This package has been made to compare some graph embedding algorithms. The following methods are implemented: Laplacian EigenMaps, Locally Linear Embedding, Higher-Order Proximity preserved Embedding (HOPE), Multi-dimensional scaling of a dissimilarity matrix, Node2vec, Struc2vec, Verse, Singular Value Decomposition of the Adjacency matrix, Kama…
The Tensorflow Implementation based on CogDL.
Code for the ICDM 2019 Paper "RiWalk: Fast Structural Node Embedding via Role Identification".
Graph Embedding via Frequent Subgraphs
Variational Graph Recurrent Neural Networks - PyTorch
A curated list of awesome graph representation learning.
This repository contains Java and C++ implementations of the algorithm GEMPE introduced in the KDD 2020 paper "Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning" by Claudia Plant, Sonja Biedermann and Christian Böhm.
Transformers are Graph Neural Networks!
The Pytorch implementation of Graph convolution network (Kipf et.al. 2017) with vanilla Teacher-Student architecture of knowledge distillation (Hinton et.al 2015).
Multi-resolution representation learning of graphs.
This is the code of the paper Breaking the Expressive Bottleneck of Graph Neural Networks.
Subgraph Neural Networks (NeurIPS 2020)
Implementation of GAP: Graph Neighborhood Attentive Pooling, https://arxiv.org/abs/2001.10394. A context-sensitve graph (network) representation learning algorithm that relies only on the structure of the graph.
This is the source code of "Network Embedding with Completely-Imbalanced Labels". TKDE2020
Code for paper https://arxiv.org/abs/2102.13186
Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxiv.org/abs/2006.09252
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