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node2vec: Scalable Feature Learning for Networks
Aditya Grover, Jure Leskovec, 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
node2vec learns a vector for every node of a network from biased random walks, so standard machine learning can be applied to nodes and edges.
What it's used for here
The inductive-methods survey proposes it for finding word classes in the manuscript's word-adjacency network.
Cited by on this site 1 record
1 methods surveys
SRCInductive methods
Cite it
Grover, A., & Leskovec, J. (2016). node2vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939754
@article{grover2016node2vec,
author = {Grover, Aditya and Leskovec, Jure},
title = {node2vec: Scalable Feature Learning for Networks},
journal = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2016},
doi = {10.1145/2939672.2939754},
url = {https://doi.org/10.1145/2939672.2939754}
}TY - JOUR TI - node2vec: Scalable Feature Learning for Networks AU - Grover, Aditya AU - Leskovec, Jure PY - 2016 JO - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining DO - 10.1145/2939672.2939754 UR - https://doi.org/10.1145/2939672.2939754 ER -
Grover, Aditya and Jure Leskovec. “node2vec: Scalable Feature Learning for Networks.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016). https://doi.org/10.1145/2939672.2939754.