The attribute network not only has a complex topology, but its nodes also contain rich
attribute information. Attribute network embedding methods extract both network topology …

Graph neural networks (GNNs) have shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …

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 …

Network embedding (NE), aiming to embed a network into a low dimensional latent
representation while preserving the inherent structural properties of the network, has …

Complex networks with node attribute information are employed to represent complex
relationships between objects. Research of attributed network embedding fuses the …

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 …

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 …

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 …

Network embedding is to learn low-dimensional representations of nodes while preserving
necessary information for network analysis tasks. Though representations preserving both …