Graph neural networks (GNNs) have shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …

Attributed network embedding has attracted plenty of interest in recent years. It aims to learn
task-independent, low-dimensional, and continuous vectors for nodes preserving both …

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

Network embedding is to learn low-dimensional representations of nodes while preserving
necessary information for network analysis tasks. Though representations preserving both …

Network embedding which aims to learn a low dimensional representation of nodes is a
powerful technique for network analysis. While network embedding for networks with …

Attributed network embedding enables to generate low-dimensional representations of
network objects by leveraging both network structure and attribute data. However, how to …

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 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 …