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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:.
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: