International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 2, 8 Apr. 2026
Cover page and Table of Contents: PDF (size: 3196KB)
An Innovative Method for Detecting Fake News Distribution Sources based on Machine Learning Technology and Graph Theory
PDF (3196KB), PP.149-180
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Author(s)
Mariia Nazarkevych
1
Victoria Vysotska
1
Vasyl Lytvyn
1
Dmytro Uhryn
2,*
Zhengbing Hu
3
Index Terms
Fake, Leiden Method, Louvain Method, Clusters, K-means, Social Networks, Information Security
Abstract
An innovative approach to identifying rapidly spreading false information is to create a targeted graph and its subsequent clustering. A method for detecting rapidly spreading fake messages in social networks has been developed. K-means, Louvain, and Leiden algorithms were applied to identify large communities in graphs, enabling the rapid detection of fake news. A modified fake news detection algorithm based on k-means and Leiden can group fake news clusters, enabling rapid identification of widely spreading news. The combination of Leiden for structural analysis of communities and SVM for classification provides an optimal balance between accuracy (F1-score = 0.87) and completeness of fake detection (Recall = 97%), allowing the system to be used both for analysing large datasets and for monitoring new publications. The Lei-den algorithm demonstrated the highest modularity (Q = 0.7212), which is 4.8% better than Louvain (Q = 0.6884), and detected 40 structural communities. The modified method has a lower modularity (Q = 0.5584), since modularity is not calculated for K-means.
Cite This Paper
Mariia Nazarkevych, Victoria Vysotska, Vasyl Lytvyn, Dmytro Uhryn, Zhengbing Hu, "An Innovative Method for Detecting Fake News Distribution Sources based on Machine Learning Technology and Graph Theory", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.2, pp.149-180, 2026. DOI:10.5815/ijcnis.2026.02.09
Reference
[1]Romaniuk, V. (2023). Disinformation narratives of hate as a tool of escalating Russia's war against Ukraine (based on stopfake fact-checking project materials). Sõjateadlane (Estonian Journal of Military Studies), 23, 143–157. https://www.kvak.ee/sojateadlane.
[2]Devarajan, G. G., Nagarajan, S. M., Amanullah, S. I., Mary, S. S. A., & Bashir, A. K. (2023). AI-assisted deep NLP-based approach for prediction of fake news from social media users. IEEE Transactions on Computational Social Systems, 11(4), 4975-4985.
[3]Venkataramanan, V., Nayyar, A., Mishra, P., Raut, A., Shah, V. S., & Vanage, V. (2026). Hca-fnd: a hybrid two-tiered approach for fake news detection using machine learning and natural language processing. Multimedia Systems, 32(1), 65.
[4]Prachi, N. N., Habibullah, M., Rafi, M. E. H., Alam, E., & Khan, R. (2022). Detection of fake news using machine learning and natural language processing algorithms. Journal of Advances in Information Technology, 13(6).
[5]Mundra, S., Reddy, J., Mundra, A., Mittal, N., Vidyarthi, A., & Gupta, D. (2023). An automated data-driven machine intelligence framework for mining knowledge to classify fake news using NLP. ACM Transactions on Asian and Low-Resource Language Information Processing.
[6]Bai, X., Ran, J., Ma, S., & Gao, S. (2026). The probabilistic response of dual-layer stochastic complex propagation networks. International Journal of Biomathematics.
[7]Jerez, E., Jurado, F., & Moreno-Llorena, J. (2026). A Human-Centred Profiling Approach to Characterise User Roles in Information Propagation on Online Social Networks. ACM Transactions on the Web.
[8]Mao, K., Li, H., Li, H., Zhu, Q., Hua, B., Chen, X., ... & Briso-Rodríguez, C. (2026). Measurement-Driven Cluster Power Generation Method for A Hybrid A2G Channel Model. IEEE Transactions on Communications.
[9]Yang, G., Zheng, W., Che, C., & Wang, W. (2020). Graph-based label propagation algorithm for community detection. International Journal of Machine Learning and Cybernetics, 11(6), 1319-1329.
[10]Zheng, M., Domanskyi, S., Piermarocchi, C., & Mias, G. I. (2021). Visibility graph based temporal community detection with applications in biological time series. Scientific reports, 11(1), 5623.
[11]Shokrzadeh, Z., Feizi-Derakhshi, M. R., Balafar, M. A., & Bagherzadeh Mohasefi, J. (2023). Graph‐Based Recommendation System Enhanced by Community Detection. Scientific Programming, 2023(1), 5073769.
[12]Aghelie, A., Sorooshian, S., & Azizan, N. A. (2016). Research gap in sustainopreneurship. Indian journal of Science and Technology, 9(12), 1-6.
[13]Shevchenko, V. (2018). Fact-checking and verification in the journalism activity. Obraz, 1(27), 140–153. https://doi.org/10.21272/2415-8496-2018-1(27)-140-153.
[14]Doropoulos, S., Karapalidou, E., Charitidis, P., Karakeva, S., & Vologiannidis, S. (2025). Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis. Applied Sciences, 15(14), 8059.
[15]Li, X. (2025). Translator's Centrality and Ideology in Chinese-English Bilingual News: A Case Study of Texts from the China Daily Website. Arts, Culture and Language, 1(3).
[16]Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. K. (2025). Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing Models. Future Internet, 17(1).
[17]Ji, L., & Xie, H. (2025). A modified method on improving power grid resilience based on the Louvain algorithm. Sustainable Energy, Grids and Networks, 101883.
[18]Anuar, S. H. H., Abas, Z. A., Yunos, N. M., Zaki, N. H. M., Hashim, N. A., Mokhtar, M. F., ... & Nizam, A. F. (2021, December). Comparison between Louvain and Leiden algorithm for network structure: a review. In Journal of Physics: Conference Series (Vol. 2129, No. 1, p. 012028). IOP Publishing.
[19]Traag, V. A., Waltman, L., & Van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 1-12.
[20]Singlan, N., Abou Choucha, F., & Pasquier, C. (2025). A new Similarity Based Adapted Louvain Algorithm (SIMBA) for active module identification in p-value attributed biological networks. Scientific Reports, 15(1), 11360.
[21]Sharma, B., Kathirvel, P., & Hegde, S. R. (2025). Identifying Communities in the Virus–Host Protein–Protein Interaction Networks. In Computational Virology (pp. 307-319). New York, NY: Springer US.
[22]Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008.
[23]Hernández, R., Gutiérrez, I., & Castro, J. (2025). Social Network Analysis: A Novel Paradigm for Improving Community Detection. International Journal of Computational Intelligence Systems, 18(1), 87.
[24]https://github.com/KaiDMML/FakeNewsNet.
[25]https://www.kaggle.com/datasets/saurabhshahane/fake-news-classification.
[26]https://www.cs.ucsb.edu/~william/data/liar_dataset.zip.
[27]Seifikar, M., Farzi, S., & Barati, M. (2020). C-blondel: an efficient louvain-based dynamic community detection algorithm. IEEE Transactions on Computational Social Systems, 7(2), 308-318.
[28]Handrizal, H., Sihombing, P., Nababan, E. B., & Budiman, M. A. (2025). AN IMPROVEMENT OF THE LEIDEN ALGORITHM FOR INFLUENCER DETECTION. Eastern-European Journal of Enterprise Technologies, 133(2).
[29]Medykovsky, M., Droniuk, I., Nazarkevich, M., & Fedevych, O. Modelling the pertubation of traffic based on ateb-functions. In Computer Networks: 20th International Conference, CN 2013, Lwówek Śląski, Poland, June 17-21, 2013. Proceedings 20 (pp. 38-44). Springer Berlin Heidelberg.
[30]Nasution, N., Hasan, M. A., & Nasution, F. B. (2025). Predicting Heart Disease Using Machine Learning: An Evaluation of Logistic Regression, Random Forest, SVM, and KNN Models on the UCI Heart Disease Dataset. IT Journal Research and Development, 9(2), 140-150.
[31]Do, D. H., & Phan, T. H. D. (2025). An improvement on the Louvain algorithm using random walks. Journal of Combinatorial Optimization, 50(2), 14. https://doi.org/10.1007/s10878-025-01337-9.
[32]Nazarkevych, M., Oliiarnyk, R., Nazarkevych, H., Kramarenko, O., Onyshschenko, I. (2016, August). The method of encryption based on Ateb-functions. In 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP) (pp. 129-133). IEEE.
[33]Nazarkevych, M., Kynash, Y., Oliarnyk, R., Klyujnyk, I., Nazarkevych, H. (2017, May). Application perfected wave tracing algorithm. In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON) (pp. 1011-1014). IEEE.
[34]Vysotska, V., Przystupa, K., Chyrun, L., Vladov, S., Ushenko, Y., Uhryn, D., & Hu, Z. (2024). Disinformation, fakes and propaganda identifying methods in online messages based on NLP and machine learning methods. Int J Comput Netw Inf Secur (IJCNIS), 16(5), 57-85.
[35]Vysotska, V., Przystupa, K., Kulikov, Y., Chyrun, S., Ushenko, Y., Hu, Z., & Uhryn, D. (2025). Recognising Fakes, Propaganda and Disinformation in Ukrainian content based on NLP and Machine-learning Technology. International Journal of Computer Network and Information Security, 17(1), 92-127.
[36]Nazarkevych, M., Vysotska, V., Myshkovskyi, Y., Nakonechnyi, N., & Nazarkevych, A. (2024). Model for forecasting the development of information threats in the cyberspace of Ukraine. Ceur Workshop Proceedings, Vol-3826. https://ceur-ws.org/Vol-3826/short13.pdf
[37]Vysotska, V., & Nazarkevych, M. (2025). Development of an information technology for detecting the sources and networks of disinformation dissemination in cyberspace based on machine learning methods. Eastern-European Journal of Enterprise Technologies, 4(2 (136), 35–51. https://doi.org/10.15587/1729-4061.2025.335501