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

Network representation learning or network embedding, which targets at learning the low-
dimension representation of graph-based data, has attracted wide attention due to its …

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

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 distributed vector representations of nodes in a network.
The problem of network embedding is fundamentally important. It plays crucial roles in many …

Network embedding, also known as graph embedding and network representation learning,
is an effective method for representing graphs or network data in a low-dimensional 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 …

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 represent network structural and attributed information with low-
dimensional vectors, which has been demonstrated to be beneficial for many 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 …