Projects sharing features with Diff2vec

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

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).

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).

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: