Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Review A Qualitative Survey on Community Detection Attack Algorithms(Mdpi, 2024) Tekin, Leyla; Bostanoglu, Belgin ErgencCommunity 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.Article Citation - WoS: 1Citation - Scopus: 1Bdac: Boundary-Driven Approximations of K-Cliques(Mdpi, 2024) Calmaz, Busra; Bostanoglu, Belgin ErgencClique counts are crucial in applications like detecting communities in social networks and recurring patterns in bioinformatics. Counting k-cliques-a fully connected subgraph with k nodes, where each node has a direct, mutual, and symmetric relationship with every other node-becomes computationally challenging for larger k due to combinatorial explosion, especially in large, dense graphs. Existing exact methods have difficulties beyond k = 10, especially on large datasets, while sampling-based approaches often involve trade-offs in terms of accuracy, resource utilization, and efficiency. This difficulty becomes more pronounced in dense graphs as the number of potential k-cliques grows exponentially. We present Boundary-driven approximations of k-cliques (BDAC), a novel algorithm that approximates k-clique counts without using recursive procedures or sampling methods. BDAC offers both lower and upper bounds for k-cliques at local (per-vertex) and global levels, making it ideal for large, dense graphs. Unlike other approaches, BDAC's complexity remains unaffected by the value of k. We demonstrate its effectiveness by comparing it with leading algorithms across various datasets, focusing on k values ranging from 8 to 50.