Simple reference implementation of GraphSAGE.
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Updated
Mar 23, 2018 - Python
Simple reference implementation of GraphSAGE.
A list of data mining and machine learning papers that I implemented in 2019.
Simple algorithm for generating graph nodes embeddings
Repository for the paper "Unsupervised Graph Embedding based on Node Similarity"
Variational Graph Recurrent Neural Networks - PyTorch
A Capsule Network-based Model for Learning Node Embeddings (CIKM 2020)
Scalable symbolic node representation learner, mirror of https://github.com/smeznar/SNoRe
Representation learning-based graph alignment based on implicit matrix factorization and structural embeddings
Source code for fact-checking using node embeddings of dependency trees
spectralembeddings is a python library which is used to generate node embeddings from Knowledge graphs using GCN kernels and Graph Autoencoders. Variations include VanillaGCN,ChebyshevGCN and Spline GCN along with SDNe based Graph Autoencoder.
My implementation of Deepwalk in PyTorch
New Algorithms for Learning on Hypergraphs
**official Code Implementation Of "GIUnet : Graph Isomorphic Unet " .
A sparsity aware implementation of "Biological Network Comparison Using Graphlet Degree Distribution" (Bioinformatics 2007)
An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".
A generator for unit disk graphs conditioned on concave hull cover.
An implementation of "Network Representation Learning with Rich Text Information" (IJCAI '15).
The reference implementation of "Multi-scale Attributed Node Embedding". (Journal of Complex Networks 2021)
A sparsity aware implementation of "Enhanced Network Embedding with Text Information" (ICPR 2018).
An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).
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