Attributed network embedding aims to learn representations of nodes and their attributes in
a low-dimensional space that preserves their semantics. The existing embedding models …

Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the
network structure but also node attributes can be preserved in the embedding space …

Network embedding is a technique that aims at inferring the low-dimensional
representations of nodes in a semantic space. In this article, we study the problem of …

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

Attributed network embedding has attracted plenty of interest in recent years. It aims to learn
task-independent, low-dimensional, and continuous vectors for nodes preserving both …

Network embedding aims at learning a low-dimensional dense vector for each node in the
network. In recent years, it has attracted great research attention due to its wide applications …

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 …

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 …

Attributed network embedding aims to learn low-dimensional node representations from
both network structure and node attributes. Existing methods can be categorized into two …

Existing embedding methods for attributed networks aim at learning low-dimensional vector
representations for nodes only but not for both nodes and attributes, resulting in the fact that …