edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Abstract Background Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is ex...

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Bibliographic Details
Main Authors: Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Ying Ding, Qi Yu
Format: Article
Language:English
Published: BMC 2019-06-01
Series:BMC Bioinformatics
Subjects:
Knowledge graph
Heterogeneous network
Biomedical knowledge discovery
Representation learning
Graph embedding
Node embedding
Online Access:http://link.springer.com/article/10.1186/s12859-019-2914-2

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http://link.springer.com/article/10.1186/s12859-019-2914-2

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