A Tutorial on Network Embeddings

TL;DR

This paper provides a comprehensive overview of network embedding techniques, categorizing recent methods, discussing their properties, applications, and future directions in graph analysis tasks.

Contribution

It offers a structured survey of recent network embedding methods, highlighting their scenarios, properties, and applications, serving as a valuable resource for researchers.

Findings

Network embeddings enable effective graph analysis tasks.

Different scenarios require tailored embedding methods.

Future research directions include heterogeneous networks and dynamic embeddings.

Abstract

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.