Network embedding which assigns nodes in networks to lowdimensional representations
has received increasing attention in recent years. However, most existing approaches …

Attributed information network embedding (AINE) has been widely used in network analysis.
Existing AINE methods mainly focus on preserving network proximities and minimizing the …

The purpose of attribute network representation learning is to learn the low-dimensional
dense vector representation of nodes by combining structure and attribute information. The …

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 …

Network embedding plays a critical role in many applications. Node classification, link
prediction, and network visualization are examples of such applications. Attributed network …

Network embedding aims to learn low-dimensional representations for nodes in social
networks, which can serve many applications, such as node classification, link prediction …

Attributed network embedding (ANE) can learn low-dimensional embeddings for nodes in
attributed graphs, which can facilitate several data analysis tasks. However, the existing …

Network embedding transforms a network into a low-dimensional space, in which the
information of network can be stored, it is an effective approach to solve the problems of …

Network embedding, as a promising tool, aims to learn low-dimensional embeddings for
nodes in a network. Most existing methods work well when the topological structure is …

Attributed networks are ubiquitous in the real world, such as social networks. Therefore,
many researchers take the node attributes into consideration in the network representation …