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Review

A Qualitative Survey on Community Detection Attack Algorithms

by
Leyla Tekin
and
Belgin Ergenç Bostanoğlu
*
Department of Computer Engineering, Izmir Institute of Technology, 35433 Izmir, Turkey
*
Author to whom correspondence should be addressed.
Symmetry 2024, 16(10), 1272; https://doi.org/10.3390/sym16101272
Submission received: 31 August 2024 / Revised: 20 September 2024 / Accepted: 24 September 2024 / Published: 26 September 2024
(This article belongs to the Section Computer)

Abstract

Community detection enables the discovery of more connected segments of complex networks. This capability is essential for effective network analysis. But, it raises a growing concern about the disclosure of user privacy since sensitive information may be over-mined by community detection algorithms. To address this issue, the problem of community detection attacks has emerged to subtly perturb the network structure so that the performance of community detection algorithms deteriorates. Three scales of this problem have been identified in the literature to achieve different levels of concealment, such as target node, target community, or global attack. A broad range of community detection attack algorithms has been proposed, utilizing various approaches to tackle the distinct requirements associated with each attack scale. However, existing surveys of the field usually concentrate on studies focusing on target community attacks. To be self-contained, this survey starts with an overview of community detection algorithms used on the other side, along with the performance measures employed to evaluate the effectiveness of the community detection attacks. The core of the survey is a systematic analysis of the algorithms proposed across all three scales of community detection attacks to provide a comprehensive overview. The survey wraps up with a detailed discussion related to the research opportunities of the field. Overall, the main objective of the survey is to provide a starting and diving point for scientists.
Keywords: community hiding; community detection attack; target node attack; target community attack; global attack community hiding; community detection attack; target node attack; target community attack; global attack

Share and Cite

MDPI and ACS Style

Tekin, L.; Bostanoğlu, B.E. A Qualitative Survey on Community Detection Attack Algorithms. Symmetry 2024, 16, 1272. https://doi.org/10.3390/sym16101272

AMA Style

Tekin L, Bostanoğlu BE. A Qualitative Survey on Community Detection Attack Algorithms. Symmetry. 2024; 16(10):1272. https://doi.org/10.3390/sym16101272

Chicago/Turabian Style

Tekin, Leyla, and Belgin Ergenç Bostanoğlu. 2024. "A Qualitative Survey on Community Detection Attack Algorithms" Symmetry 16, no. 10: 1272. https://doi.org/10.3390/sym16101272

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