Attention-based network embedding with higher-order weights and node attributes
Network embedding aspires to learn a low-dimensional vector of each node in networks, which can apply to diverse data mining tasks. In real-life, many networks include rich attributes and temporal information. However, most existing embedding approaches ignore either temporal information or network...
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Main Authors: | , , , , |
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Format: | Article |
Published: |
Wiley
2024
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Subjects: | |
Online Access: | http://eprints.um.edu.my/46062/ |
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Institution: | Universiti Malaya |