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PecanPy: a fast, efficient, and parallelized Python implementation of node2vec
2021
Bioinformatics
Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. Node2vec is the most widely used method for node embedding. However, its original Python and C ++ implementations scale poorly with network density, failing for dense biological networks with hundreds of millions of edges. We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and
doi:10.1093/bioinformatics/btab202
pmid:33760066
pmcid:PMC8504639
fatcat:ed6psnd4b5hq5fekiaqfs6aapm