Hierarchical Representation Learning for Attributed Networks
Shu Guo Zhao, Ziwei Du, Jie Chen, Yanping Zhang, Jie Tang, Philip S. Yu · 2022 IEEE 38th International Conference on Data Engineering (ICDE) · 2022
Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, such as structural similarity, attribute similarity, etc. The low-dimensional vector of the node can be used as the input of the machine learning algorithm and applied to a lot of downstream tasks, such as node classification and link prediction, benefits plenty of practical applications.