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Perozzi et al., 2017 - Google Patents

Don't walk, skip! online learning of multi-scale network embeddings

Perozzi et al., 2017

View PDF
Document ID
10731793347567728683
Author
Perozzi B
Kulkarni V
Chen H
Skiena S
Publication year
Publication venue
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

External Links

Snippet

We present WALKLETS, a novel approach for learning multiscale representations of vertices in a network. In contrast to previous works, these representations explicitly encode multi- scale vertex relationships in a way that is analytically derivable. WALKLETS generates these …
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Classifications

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    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
    • G—PHYSICS
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