Heterogeneous graph representation learning and applications [electronic resource] / Chuan Shi, Xiao Wang, Philip S. Yu.
Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because...
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| Online Access: |
Full Text (via Springer) |
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| Main Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
2021.
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| Series: | Artificial intelligence: foundations, theory, and algorithms.
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| Subjects: |
Internet
Full Text (via Springer)Online
| Call Number: |
QA76.88
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| QA76.88 | Available |