Home >

news Help

Publication Information


Title
Japanese: 
English:Network Embedding Based on a Quasi-Local Similarity Measure 
Author
Japanese: Liu Xin, Natthawut Kertkeidkachorn, 村田剛志, Kyoung-Sook Kim, Julien Leblay, Steven Lynden.  
English: Liu Xin, Natthawut Kertkeidkachorn, Tsuyoshi MURATA, Kyoung-Sook Kim, Julien Leblay, Steven Lynden.  
Language English 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page         pp. 429-440
Published date Aug. 28, 2018 
Publisher
Japanese: 
English:Springer 
Conference name
Japanese: 
English:15th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2018) 
Conference site
Japanese: 
English:Nanjing 
Official URL https://link.springer.com/chapter/10.1007/978-3-319-97304-3_33
 
DOI https://doi.org/10.1007/978-3-319-97304-3_33
Abstract Network embedding based on the random walk and skip-gram model such as the DeepWalk and Node2Vec algorithms have received wide attention. We identify that these algorithms essentially estimate the node similarities by random walk simulation, which is unreliable, inefficient, and inflexible. We propose to explicitly use node similarity measures instead of random walk simulation. Based on this strategy and a new proposed similarity measure, we present a fast and scalable algorithm AA+Emb. Experiments show that AA+Emb outperforms state-of-the-art network embedding algorithms on several commonly used benchmark networks.

©2007 Tokyo Institute of Technology All rights reserved.