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 …

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

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 …

Real-world networks contain rich semantic information, making attribute network embedding
a vital tool for their analysis and exploitation. Nevertheless, this embedding process …

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

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

Attributed network embedding, which aims to map the structural and attribute information
into a latent vector space jointly, has attracted a surge of research attention in recent years …

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 …

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

Network embedding, which aims to generate dense, low-dimensional and representative
embedding representations for all nodes in the network, is a crucial step for various AI …