2012年
Hash-based structural similarity for semi-supervised Learning on attribute graphs.
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)
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- 開始ページ
- 3009
- 終了ページ
- 3012
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- 出版者・発行元
- IEEE
We present an efficient method to compute similarity between graph nodes by comparing their neighborhood structures rather than proximity. The key is to use a hash for avoiding expensive subgraph comparison. Experiments show that the proposed algorithm performs well in semi-supervised node classification.
- リンク情報
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- DBLP
- https://dblp.uni-trier.de/rec/conf/icpr/HidoK12
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000343660603019&DestApp=WOS_CPL
- URL
- http://ieeexplore.ieee.org/document/6460798/
- URL
- https://dblp.uni-trier.de/conf/icpr/2012
- URL
- https://dblp.uni-trier.de/db/conf/icpr/icpr2012.html#HidoK12
- ID情報
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- ISSN : 1051-4651
- DBLP ID : conf/icpr/HidoK12
- Web of Science ID : WOS:000343660603019