node2vec Algorithm
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A node2vec Algorithm is a graph embedding algorithm that ...
- See: node2vec System.
References
2018
- (Qiu et al., 2018) ⇒ Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. (2018). “Network Embedding As Matrix Factorization: Unifying DeepWalk, LINE, PTE, and Node2vec.” In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ISBN:978-1-4503-5581-0 doi:10.1145/3159652.3159706
2017
- http://snap.stanford.edu/node2vec/
- QUOTE: Learning useful representations from highly structured objects such as graphs is useful for a variety of machine learning applications. Besides reducing the engineering effort, these representations can lead to greater predictive power.
The node2vec framework learns low-dimensional representations for nodes in a graph by optimizing a neighborhood preserving objective. The objective is flexible, and the algorithm accomodates for various definitions of network neighborhoods by simulating biased random walks. Specifically, it provides a way of balancing the exploration-exploitation tradeoff that in turn leads to representations obeying a spectrum of equivalences from homophily to structural equivalence
- QUOTE: Learning useful representations from highly structured objects such as graphs is useful for a variety of machine learning applications. Besides reducing the engineering effort, these representations can lead to greater predictive power.
2016
- (Grover & Leskovec, 2016) ⇒ Aditya Grover, and Jure Leskovec. (2016). “node2vec: Scalable Feature Learning for Networks.” In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-4232-2 doi:10.1145/2939672.2939754