Network embedding aims to map a complex network into a low-dimensional vector space
while maximally preserving the properties of the original network. An attributed network is a …

Attributed network embedding is the task to learn a lower dimensional vector representation
of the nodes of an attributed network, which can be used further for downstream network …

Attributed network embedding aims to learn low dimensional node representations by
combining both the network's topological structure and node attributes. Most of the existing …

Network representation learning, also called network embedding, aiming to learn low
dimensional vectors for nodes while preserving essential properties of the network, benefits …

Network embedding has attracted a surge of attention in recent years. It is to learn the low-
dimensional representation for nodes in a network, which benefits downstream tasks such …

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 aims to seek low-dimensional vector representations for
nodes in a network, such that original network topological structure and node attribute …

Network embedding, which aims at learning low-dimensional representations of nodes in a
network, has drawn much attention for various network mining tasks, ranging from link …

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 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 …