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 …

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 plays a crucial role in network analysis to provide effective
representations for a variety of learning tasks. Existing attributed network embedding …

Network embedding aims to learn a vector for each node while preserves inherent
properties of the network. Topological structure and node attributes are both critical for …

Network embedding is to learn a low-dimensional representation for a network in order to
capture intrinsic features of the network. It has been applied to many applications, eg …

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 …

Network embedding aims to map vertices in a complex network into a continuous low-
dimensional vector space. Meanwhile, the original network structure and inherent properties …

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 …

The fundamental purpose of network embedding is to automatically encode each node in a
network as a low-dimensional vector, while at the same time preserving certain …