Open-source alternatives to Walklets

A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).

Reference implementation of Diffusion2Vec (Complenet 2018) built on Gensim and NetworkX.

The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".

An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".

The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).

A SciPy implementation of "GraRep: Learning Graph Representations with Global Structural Information" (WWW 2015).

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

An implementation of "Community Preserving Network Embedding" (AAAI 2017)

The reference implementation of "Multi-scale Attributed Node Embedding". (Journal of Complex Networks 2021)

A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).

A sparsity aware implementation of "Enhanced Network Embedding with Text Information" (ICPR 2018).

CPU-based multi-thread implementation of Task-guided and Path-augmented Heterogeneous Network Embedding

BiNE: Bipartite Network Embedding

ICE: Item Concept Embedding

This project has been moved to https://github.com/cnclabs/smore.

PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

Generate embeddings from large-scale graph-structured data.

Starspace is a vector embedding framework designed for training high-dimensional representations of text and images. It functions as a machine learning system for neural ranking, text classification, and knowledge graph embedding, mapping different object types into a shared numerical space to facilitate retrieval and prediction tasks. The system includes specialized tools for knowledge graph completion and link prediction by representing entities and their relationships within a multi-relational vector space. It further provides capabilities for semantic content recommendation and large-scal

This repository provides a reference implementation of node2vec as described in the paper: