تشخیص انتشار شایعه در شبکه های پیچیده بر اساس مدل ILSR و درجه گره ها
محورهای موضوعی : مهندسی الکترونیک
خسرو احمدی
1
,
طالب خفایی
2
,
مازیار گنجو
3
1 - گروه فنی مهندسی دانشگاه آزاد اسلامی بوشهر
2 - گروه مهندسی کامپیوتر، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
3 - گروه مهندسی فناوری اطلاعات، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
کلید واژه: شبکههای پیچیده, مدل ILSR, شبکههای اجتماعی, انتشار شایعه, درجه گرهها,
چکیده مقاله :
ماهیت فراگیر پلتفرمهای شبکههای اجتماعی منجر به تولید حجم زیادی از دادهها شده است. عدم وجود محدودیت برای به اشتراک گذاشتن اطلاعات در این شبکهها باعث گسترش اطلاعات بدون توجه به اعتبار آنها میشود. چنین اطلاعات غلطی معمولاً منجر به تولید و انتشار شایعات میگردد. بنابراین، تشخیص خودکار شایعات در شبکههای اجتماعی یکی از حوزههای تحقیقاتی جذاب برای تجزیه و تحلیل این شبکهها است. این مقاله روشی را برای مقابله با انتشار شایعات در شبکههای اجتماعی بر پایه مدل شبکه عصبی خودرمزگذار و مدل انتشار اپیدمی ILSR معرفی میکند. در اینجا، مدل شبکه عصبی خودرمزگذار با چندین آستانه ابتکاری برای تشخیص اولیه شایعه اعمال شده و سپس کنترل شایعات توسط یک نسخه توسعه یافتهای از مدل شیوع اپیدمی ILSR انجام میشود. مدل پیشنهادی با نام ILSHR کاربران شبکه اجتماعی را در پنج گروه جاهل، کمین، پخشکننده، خواب زمستانی و سختگیر در نظر میگیرد. با توجه به حالات انتقال در مدل انتشار شایعه ILSHR، این مدل علاوه بر خصوصیات مربوط به گروه افراد کمین، خصوصیات گروه افراد خواب زمستانی را نیز از مدل SIHR لحاظ میکند. مکانیسمهای فراموشی و یادآوری از خواب زمستانی می تواند زمان ترمینال شایعه را به تعویق انداخته و در نهایت باعث کاهش تأثیر شایعه در شبکه اجتماعی شود. تجزیهوتحلیل روش پیشنهادی برای مدلسازی انتشار شایعات روی مجموعه داده شبکه اجتماعی سینا ویبو انجام شده است. نتایج نشان دهنده عملکرد بهتر روش پیشنهادی با دقت تشخیص 95.7% نسبت به مدلهای DGRU و DLSTM میباشد.
The pervasive nature of social networking platforms has led to the production of large amounts of data. The lack of restrictions on information sharing on these networks allows information to be spread regardless of their validity. Such misinformation usually leads to the production and dissemination of rumors. Therefore, automatic detection of rumors on social networks is one of the attractive research areas for the analysis of these networks. This paper introduces a way to deal with the spread of rumors on social media based on the autoencoder neural network model and the ILSR epidemic dissemination model. Here, the autoencoder neural network model is applied with several innovative thresholds for initial rumor detection and then rumor control is performed by an extended version of the ILSR epidemic outbreak model. The proposed model called ILSHR considers social network users in five groups: Ignorant, Lurker, Spreader, Hibernator, and Removal. According to the transmission modes in the ILSHR rumor model, in addition to the characteristics of the lurker group, this model also considers the characteristics of the hibernator group of the SIHR model. Mechanisms of forgetfulness and reminders of hibernation can delay the rumor terminal time and ultimately reduce the impact of the rumor on the social network. The proposed method for modeling the spread of rumors has been performed on the Sina Weibo social network dataset. The results show better performance of the proposed method with 95.7% detection accuracy compared to DGRU and DLSTM models.
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[1] S. Nemati, M. Sadeghzadeh and M. Ganjoo, “Use of classification models for optimize link prediction in the ego-social networks”, Journal of Communication Engineering, vol. 10,no.39, pp. 53-68, 2021 (in Persian).
[2] P. J. Witbooi, “Stability of an SEIR epidemic model with independent stochastic perturbations”, Physica A: Statistical Mechanics and its Applications, vol. 392, no. 20, pp. 4928-4936, 2013.
[3] Q. Zhang, B. Tang and S. Tang, "Vaccination threshold size and backward bifurcation of SIR model with state-dependent pulse control", Journal of Theoretical Biology, vol. 455, pp. 75-85, 2018.
[4] M. A. Nadoomi and M. Sina, “sing Ant Colony Algorithm and Pairwise Learning to Classify Attack in Intrusion Detection Systems”, Journal of Communication Engineering, vol. 9,no.36, pp. 39-53, 2020 (in Persian).
[5] C. Zheng, C. Xia, Q. Guo and M. Dehmer, "Interplay between SIR-based disease spreading and awareness diffusion on multiplex networks", Journal of Parallel and Distributed Computing, vol. 115, pp. 20-28, 2018.
[6] T. Li, S. Wang and B. Li, "Research on Suppression Strategy of Social Network Information Based on Effective Isolation", Procedia Computer Science, vol. 131, pp. 131-138, 2018.
[7] S. Daum, F. Kuhn and Y. Maus, “Rumor spreading with bounded in-degree”, Theoretical Computer Science, vol. 810, pp.43-57, 2020.
[8] A. Movloodian and H. M. Haghighi, “Assessment of 5G mobile networks in stream service”, Journal of Communication Engineering, vol. 10,no.36, pp. 13-26, 2020 (in Persian).
[9] L. Yang, Z. Li and A. Giua, “Containment of rumor spread in complex social networks,” Information Sciences, vol. 506, pp. 113-130, 2020.
[10] A. Yang, X. Huang, X. Cai, X. Zhu and L. Lu, "ILSR rumor spreading model with degree in complex network", Physica A: Statistical Mechanics and its Applications, vol. 531, p. 121807, 2019.
[11] S.M. Alzanin and A.M. Azmi, “Rumor detection in Arabic tweets using semi-supervised and unsupervised expectation–maximization”, Knowledge-Based Systems, vol. 185, pp. 104945, 2019.
[12] S. Oleiwi, G. Omran and H. Abdulshaheed, "Detecting Anomalies in Computer Networks Recurrent Neural Networks", Journal of Southwest Jiaotong University, vol. 54, no. 5, 2019.
[13] J. Ma, W. Gao, S. Joty and K. Wong, "An Attention-based Rumor Detection Model with Tree-structured Recursive Neural Networks", ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 4, pp. 1-28, 2020.
[14] N. Xu, G. Chen and W. Mao, “MNRD: A merged neural model for rumor detection in social media”, in IEEE International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1-7.
[15] R. Sicilia, S. Lo Giudice, Y. Pei, M. Pechenizkiy and P. Soda, "Twitter rumour detection in the health domain", Expert Systems with Applications, vol. 110, pp. 33-40, 2018.
[16] T.N Nguyen, C. Li and C. Niederée, “On early-stage debunking rumors on twitter: Leveraging the wisdom of weak learners”, In International Conference on Social Informatics, Springer, 2017, pp. 141-158.
[17] Y. Xu, C. Wang, Z. Dan, S. Sun and F. Dong, "Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo", Symmetry, vol. 11, no. 11, p. 1408, 2019.
[18] C. Cheadle, M. Vawter, W. Freed and K. Becker, "Analysis of Microarray Data Using Z Score Transformation", The Journal of Molecular Diagnostics, vol. 5, no. 2, pp. 73-81, 2003.
[19] D. Khattar, J.S. Goud, M. Gupta and V. Varma, “Mvae: Multimodal variational autoencoder for fake news detection”, In The world wide web conference, 2019, pp. 2915-2921.
[20] L. Zhao, H. Cui, X. Qiu, X. Wang and J. Wang, "SIR rumor spreading model in the new media age", Physica A: Statistical Mechanics and its Applications, vol. 392, no. 4, pp. 995-1003, 2013.
[21] L. Zhao, J. Wang, Y. Chen, Q. Wang, J. Cheng and H. Cui, "SIHR rumor spreading model in social networks", Physica A: Statistical Mechanics and its Applications, vol. 391, no. 7, pp. 2444-2453, 2012.
[22] L. Zhao, X. Qiu, X. Wang and J. Wang, “Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks”, Physica A: Statistical Mechanics and its Applications, vol. 392, no. 4, pp. 987-994, 2013
[23] K. Lei et al., "Understanding User Behavior in Sina Weibo Online Social Network: A Community Approach", IEEE Access, vol. 6, pp. 13302-13316, 2018.
[24] J. Chen and J. She, “An analysis of verifications in microblogging social networks--Sina Weibo”, in IEEE International Conference on Distributed Computing Systems Workshops, 2012, pp. 147-154.
[25] G. Han and W. Wang, “Mapping user relationships for health information diffusion on microblogging in China: A social network analysis of Sina Weibo”, Asian Journal of Communication, vol. 25, no. 1, pp. 65-83, 2015.
[26] N. Ruchansky, S. Seo and Y. Liu, “Csi: A hybrid deep model for fake news detection”, In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 797-806.