A curated list of network embedding techniques.

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)

Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)

From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)

Code for the paper "Fine-Grained Entity Typing in Hyperbolic Space"

Learning to represent shortest paths and other graph-based measures of node similarities with graph embeddings

Implement the node2vec algorithm using Python

An implementation of the Watset clustering algorithm in Java.

Embedding graphs in symmetric spaces

Terraphim AI: deterministic AI Assistant

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

GyroSPD: Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices

The SEMB library is an easy-to-use tool for getting and evaluating structural node embeddings in graphs.

Smooth Variational Graph Embeddings for Efficient Neural Architecture Search

GitHub repositories and users recommendations by embeddings

Vectorizing knowledge bases for entity linking

An implementation of vdist2vec model in paper A Learning Based Approach to Predict Shortest-Path Distances

Julia package to Compare Graph Embeddings