Hierarchical Representation Learning for Attributed Networks

Abstract

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, such as structural similarity, attribute similarity, etc. The low-dimensional vector of the node can be used as the input of the machine learning algorithm and applied to a lot of downstream tasks, such as node classification and link prediction, benefits plenty of practical applications.

Topics & Concepts