Zoo guide to network embedding

Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in...

Full description

Saved in:
Bibliographic Details
Published inJournal of physic, complexity Vol. 4; no. 4; pp. 42001 - 42025
Main Authors Baptista, A, Sánchez-García, R J, Baudot, A, Bianconi, G
Format Journal Article
LanguageEnglish
Published IOP Publishing 01.12.2023
IOP
Subjects
higher-order network
network analysis
network embedding
Physics
representation learning
higher-order network
network analysis
representation learning
network embedding
Online AccessGet full text

Cover

More Information
Summary:Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects.
Bibliography:JPCOMPX-100473.R1
ISSN:2632-072X
2632-072X
DOI:10.1088/2632-072X/ad0e23