Community Hiding Algorithm Based on Likelihood Analysis Method in Link Prediction

• Published in: Proceedings of the ... International Symposium on Parallel and Distributed Processing with Applications (Print) pp. 645 - 652
• Main Authors: Wang, Ya-Si, Shi, Lei-Lei, Han, Zi-Xuan, Liu, Lu, Song, Siyang, Chen, Liming
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30.10.2024
• Subjects: Blogs; Community hiding; Distributed processing; Heuristic algorithms; Link prediction; Nonoverlapping community detection; Prediction algorithms; Privacy; Probability; Social network; Social networking (online)
• ISSN: 2158-9208
• DOI: 10.1109/ISPA63168.2024.00088

In recent years, the rapid development and widespread applications of non-overlapping community discovery have led to a growing issue of privacy leakage. The core of this problem lies in the community discovery algorithms themselves. Researchers have shifted their focus toward developing community hiding algorithms. However, existing non-overlapping community hiding algorithms have mainly been studied in static networks, overlooking the prevalence of dynamic networks in real life. In this paper, we introduce a community hiding algorithm called LALH, which employs the link prediction likelihood analysis method. The algorithm consists of two parts: first, the OLPL algorithm calculates a list of link probabilities in the subsequent time scale of the network, allowing observation of important link distribution. Second, the LALH algorithm selects these crucial links for the community hiding operation. We validate the effectiveness of the LALH algorithm through experiments on three public datasets and a real Twitter dataset.