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 is to learn low-dimensional vector representations for nodes in a
network. It has shown to be effective in a variety of tasks such as node classification and link …

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

Network embedding plays a crucial role in network analysis to provide effective
representations for a variety of learning tasks. Existing attributed network embedding …

Attributed network embedding enables joint representation learning of node links and
attributes. Existing attributed network embedding models are designed in continuous …

With a surge of network data, attributed networks are widely applied for various applications.
Recently, how to embed an attributed network into a low-dimensional representation space …

Network embedding, also known as network representation learning, aims at defining low-
dimensional, continuous vector representation of nodes to maximally preserve the network …

Network embedding aims to learn the low-dimensional representations/embeddings of
vertices which preserve the structure and inherent properties of the networks. The resultant …

Attributed network embedding (ANE) attempts to represent a network in short code, while
retaining information about node topological structures and node attributes. A node's feature …

Network embedding aims to learn the low-dimensional representations for the components
in the network while maximally preserving the structure and inherent properties. Its efficiency …