How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
This repository provides a reference implementation of node2vec as described in the paper:
Implementation of ComE algorithm
The main features of andompesta/come are: Embedding Algorithms.
Projects with overlapping indexed features include: anryyang/hope. benedekrozemberczki/asne — A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018). benedekrozemberczki/attentionwalk — A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018). benedekrozemberczki/bane — A sparsity aware implementation of "Binarized Attributed Network Embedding" (ICDM 2018). benedekrozemberczki/boostedfactorization — An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019). aditya-grover/node2vec — This repository provides a reference implementation of node2vec as described in the paper:.