opinion

A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

Authors:

Zhepeng (Lionel) Li,

Olivia R. Liu ShengAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 9, Issue 1

Article No.: 1, Pages 1 - 26

https://doi.org/10.1145/3131782

Published: 25 October 2017 Publication History

Abstract

Link recommendation has attracted significant attention from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which include “People You May Know” on LinkedIn and “You May Know” on Google+. In academia, link recommendation has been and remains a highly active research area. This article surveys state-of-the-art link recommendation methods, which can be broadly categorized into learning-based methods and proximity-based methods. We further identify social and economic theories, such as social interaction theory, that underlie these methods and explain from a theoretical perspective why a link recommendation method works. Finally, we propose to extend link recommendation research in several directions that include utility-based link recommendation, diversity of link recommendation, link recommendation from incomplete data, and experimental study of link recommendation.

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Gui C(2024)Link prediction based on spectral analysisPLOS ONE10.1371/journal.pone.028738519:1(e0287385)Online publication date: 2-Jan-2024
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Zheng XNi ZZhong XLuo Y(2024)Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit InformationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318294235:1(1205-1216)Online publication date: Jan-2024
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Han JJeon BGeum Y(2024)Link Prediction Revisited: New Approach for Anticipating New Innovation Chances Using Technology ConvergenceIEEE Transactions on Engineering Management10.1109/TEM.2022.321386771(5143-5159)Online publication date: 2024
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Index Terms

A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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Link Recommendation for Social Influence Maximization
Social link recommendation systems, like “People-you-may-know” on Facebook, “Who-to-follow” on Twitter, and “Suggested-Accounts” on Instagram assist the users of a social network in establishing new connections with other users. While these systems are ...
Diversity Preference-Aware Link Recommendation for Online Social Networks
Link recommendation, such as “People You May Know” on LinkedIn, recommends links to connect unlinked online social network users. Existing link recommendation methods tend to recommend similar friends to a user but overlook the fact that different users ...
Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a ...
Utility-Based Link Recommendation for Online Social Networks

Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networks. Salient examples of link recommendation include "People You May Know" on Facebook and LinkedIn as well as "You ...

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Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 9, Issue 1

March 2018

89 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3146385

Editor:
Daniel Zeng
University of Arizona, USA

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Copyright © 2017 ACM.

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Publication History

Published: 25 October 2017

Accepted: 01 August 2017

Revised: 01 April 2017

Received: 01 February 2016

Published in TMIS Volume 9, Issue 1

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Cited By

Gui C(2024)Link prediction based on spectral analysisPLOS ONE10.1371/journal.pone.028738519:1(e0287385)Online publication date: 2-Jan-2024
https://doi.org/10.1371/journal.pone.0287385
Zheng XNi ZZhong XLuo Y(2024)Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit InformationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318294235:1(1205-1216)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3182942
Han JJeon BGeum Y(2024)Link Prediction Revisited: New Approach for Anticipating New Innovation Chances Using Technology ConvergenceIEEE Transactions on Engineering Management10.1109/TEM.2022.321386771(5143-5159)Online publication date: 2024
https://doi.org/10.1109/TEM.2022.3213867
Wang ZChai YSun CRui XMi HZhang XYu P(2024)A Weighted Symmetric Graph Embedding Approach for Link Prediction in Undirected GraphsIEEE Transactions on Cybernetics10.1109/TCYB.2022.318181054:2(1037-1047)Online publication date: Feb-2024
https://doi.org/10.1109/TCYB.2022.3181810
Zhao KZhao D(2024)Link Prediction in Dynamic Real-Life Friendship Networks: A Case Study2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)10.1109/CCAI61966.2024.10603341(137-141)Online publication date: 24-May-2024
https://doi.org/10.1109/CCAI61966.2024.10603341
Ran YLiu JZhang Y(2023)Integrating Users’ Contextual Engagements with Their General PreferencesINFORMS Journal on Computing10.1287/ijoc.2023.128435:3(614-632)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1287/ijoc.2023.1284
Wei XLiu YSun JJiang YTang QYuan K(2023)Dual Subgraph-Based Graph Neural Network for Friendship Prediction in Location-Based Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/355498117:3(1-28)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3554981
Zhang QLiao WZhang GYuan BLu J(2023)A Deep Dual Adversarial Network for Cross-Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313295335:4(3266-3278)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3132953
Brinkmann LBaumann FBonnefon JDerex MMüller TNussberger ACzaplicka AAcerbi AGriffiths THenrich JLeibo JMcElreath ROudeyer PStray JRahwan I(2023)Machine cultureNature Human Behaviour10.1038/s41562-023-01742-27:11(1855-1868)Online publication date: 20-Nov-2023
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He CCheng JFei XWeng YZheng YTang Y(2023)Community preserving adaptive graph convolutional networks for link prediction in attributed networksKnowledge-Based Systems10.1016/j.knosys.2023.110589272:COnline publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110589
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