node2vec
Algorithmic Framework for Representational Learning on Graphs
Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | node2vec_0.1.0.tar.gz |
30.8 KiB |
0.1.0 |
rolling linux/noble R-4.5 | node2vec_0.1.0.tar.gz |
30.7 KiB |
0.1.0 |
rolling source/ R- | node2vec_0.1.0.tar.gz |
4.9 KiB |
0.1.0 |
latest linux/jammy R-4.5 | node2vec_0.1.0.tar.gz |
30.8 KiB |
0.1.0 |
latest linux/noble R-4.5 | node2vec_0.1.0.tar.gz |
30.7 KiB |
0.1.0 |
latest source/ R- | node2vec_0.1.0.tar.gz |
4.9 KiB |
0.1.0 |
2026-04-26 source/ R- | node2vec_0.1.0.tar.gz |
4.9 KiB |
0.1.0 |
2026-04-23 source/ R- | node2vec_0.1.0.tar.gz |
4.9 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | node2vec_0.1.0.zip |
34.1 KiB |
0.1.0 |
2025-04-20 source/ R- | node2vec_0.1.0.tar.gz |
4.9 KiB |