Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while …
D Jin, R Wang, T Wang, D He, W Ding… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Network embedding which aims to learn a low dimensional representation of nodes is a powerful technique for network analysis. While network embedding for networks with …
Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node …
H Shan, D Jin, P Jiao, Z Liu, B Li… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Network embedding (NE), aiming to embed a network into a low dimensional latent representation while preserving the inherent structural properties of the network, has …
D Chen, M Nie, H Zhang, Z Wang, D Wang - Mathematics, 2022 - mdpi.com
Complex networks with node attribute information are employed to represent complex relationships between objects. Research of attributed network embedding fuses the …
W Gao, P Wu, L Pan - Proceedings of the 2021 IEEE/ACM 8th …, 2021 - dl.acm.org
Clustering is the basis of many complex network analysis and application tasks. Preserving the clustering properties in network representation space contributes to a better clustering …
K Dong, T Huang, L Zhou, L Wang, H Chen - International conference on …, 2021 - Springer
The attributed network embedding aims to learn the latent low-dimensional representations of nodes, while preserving the neighborhood relationship of nodes in the network topology …
H Gao, H Huang - Twenty-Seventh International Joint Conference on …, 2018 - par.nsf.gov
Network embedding has attracted a surge of attention in recent years. It is to learn the low- dimensional representation for nodes in a network, which benefits downstream tasks such …
C Zheng, L Pan, P Wu - IEEE Transactions on Neural Networks …, 2021 - ieeexplore.ieee.org
Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both …