DeepWalk

Bryan Perozzi; Rami Al-Rfou; Steven Skiena · OpenAlex · 2014

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Author / responsible party

Bryan Perozzi; Rami Al-Rfou; Steven Skiena

Publisher

OpenAlex

Year

2014

Language

English

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We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs.

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APA 7

Perozzi, B, Al-Rfou, R, & Skiena, S. (2014). DeepWalk. https://doi.org/10.1145/2623330.2623732

MLA

Perozzi, Bryan, et al. "DeepWalk." 2014. https://doi.org/10.1145/2623330.2623732.

Chicago

Perozzi, Bryan, Rami Al-Rfou, and Steven Skiena. 2014. "DeepWalk.". https://doi.org/10.1145/2623330.2623732.

Harvard

Perozzi, B, Al-Rfou, R. and Skiena, S. 2014, DeepWalk, OpenAlex, available at: https://doi.org/10.1145/2623330.2623732 [Accessed 7 Aug. 2026].

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Resource details

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Title: DeepWalk

Author / contributors: Bryan Perozzi; Rami Al-Rfou; Steven Skiena

Publisher: OpenAlex

Publication year: 2014

Language: English

Subjects

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Artificial intelligence; Computer science; Feature (linguistics); Statistical model; Feature learning; Latent variable; Natural language processing; Statistical learning; Deep learning; Representation (politics); Feature vector; Latent semantic analysis