Improving the Accuracy of Locally Differentially Private Community Detection by Order-consistent Data Perturbation-Reference-Cited by-同舟云学术

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Improving the Accuracy of Locally Differentially Private Community Detection by Order-consistent Data Perturbation

Published:2024-07-10 Issue: Volume:35 Page:1743-1752
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Guo Taolin¹^ORCID,Peng Shunshun¹^ORCID,Zhang Zhejian²^ORCID,Yang Mengmeng³^ORCID,Lam Kwok-Yan⁴^ORCID

Affiliation:

1. College of Computer and Information Science, Chongqing Normal University, Chongqing, China

2. College of Computer Science, Chongqing University, Chongqing, China

3. Data61 of Commonwealth Scientific and Industrial Research Organization, Melbourne, Australia

4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Funder

Science and Technology Research Program of Chongqing Municipal Education Commission

Natural Science Foundation of Chongqing

National Natural Science Foundation of China

Foundation Projection of Chongqing Normal University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657836

Reference29 articles.

1. A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning;Jin Di;IEEE Trans. Knowl. Data Eng.,2023

2. A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

3. How Significant Attributes are in the Community Detection of Attributed Multiplex Networks

4. BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts

5. Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract)

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