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 …

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 …

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 …

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 to generate dense, low-dimensional and representative
embedding representations for all nodes in the network, is a crucial step for various AI …

Attributed networks can better describe the real-world complex systems where the
interaction or relationship between entities can be represented as networks and the auxiliary …

Network embedding aims to learn a latent, low-dimensional vector representations of
network nodes, effective in supporting various network analytic tasks. While prior arts on …

Network embedding effectively transforms complex network data into a low-dimensional
vector space and has shown great performance in many real-world scenarios, such as link …

Network embedding aims to learn a low-dimensional vector for each node in networks,
which is effective in a variety of applications such as network reconstruction and community …