Graph embedding with rich information through heterogeneous graph
Graph embedding, aiming to learn low-dimensional representations for nodes in graphs, has attracted increasing attention due to its critical application including node classification, link prediction and clustering in social network analysis. Most existing algorithms for graph embedding only rely on...
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| Language: | en |
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2017
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| Online Access: | http://hdl.handle.net/10754/626207 http://repository.kaust.edu.sa/kaust/handle/10754/626207 |