Deep learning for community detection: progress, challenges and opportunities

Fanzhen Liu, Shan Xue, Jia Wu*, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, Philip S. Yu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

34 Citations (Scopus)

Abstract

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain - deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.

Original languageEnglish
Title of host publicationProceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
EditorsChristian Bessiere
Place of PublicationCalifornia
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4981-4987
Number of pages7
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - 2020
Event29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan
Duration: 1 Jan 2021 → …

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2021-January
ISSN (Print)1045-0823

Conference

Conference29th International Joint Conference on Artificial Intelligence, IJCAI 2020
CountryJapan
CityYokohama
Period1/01/21 → …

Keywords

  • Machine Learning

Fingerprint

Dive into the research topics of 'Deep learning for community detection: progress, challenges and opportunities'. Together they form a unique fingerprint.

Cite this