Graph Neural Network Architectures for Dynamic Social Network Analysis and Real-Time Community Detection
Keywords:
Graph Neural Networks, Dynamic Social Networks, Community Detection, Temporal Graphs, Real-Time Analytics, Social Media MiningAbstract
Dynamic social networks, characterized by continuously evolving node and edge structures, demand advanced analytical models capable of both representation learning and efficient inference. Graph Neural Networks (GNNs) have emerged as powerful tools for learning on such structured data. This paper explores the integration of GNN architectures with dynamic social network data for real-time community detection. It compares traditional methods with GNN-based frameworks, outlines key architectural components, and provides empirical insights into performance benchmarks. Real-world applications in misinformation detection, trend prediction, and social recommendation systems are discussed.
References
Kipf, Thomas N., and Max Welling. "Semi-Supervised Classification with Graph Convolutional Networks." arXiv preprint arXiv:1609.02907, 2017.
S. B. Vinay, Natural Language Processing for Legal Documentation in Indian Languages, International Journal of Natural Language Processing (IJNLP), 2(1), 2024, 1-10.
Adamson E, Ravichandran V, Sidikou S, Walker L, Balasubramanian S and Leach J (2016). Optimization of biomaterial microenvironment for motor neuron tissue engineering. Front. Bioeng. Biotechnol. Conference Abstract: 10th World Biomaterials Congress. doi: 10.3389/conf.FBIOE.2016.01.02740
Kabilan, R. (2025). Harnessing Elastic Resource Allocation in Cloud Computing for Scalable Real-Time Analytics in Distributed Systems. Global Journal of Multidisciplinary Research and Development, 6(3), 49–53
Hamilton, William L., Rex Ying, and Jure Leskovec. "Inductive Representation Learning on Large Graphs." Advances in Neural Information Processing Systems, vol. 30, 2017.
S. B. Vinay, "AI and machine learning integration with AWS SageMaker: current trends and future prospects", International Journal of Artificial Intelligence Tools (IJAIT), vol. 1, issue 1, pp. 1-24, 2024.
Zhou, Jie, et al. "Graph Neural Networks: A Review of Methods and Applications." IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, 2020, pp. 4–24.
Kabilan R. (2021). Advancements in zero trust security models for next generation network infrastructures. ISCSITR-International Journal of Information Technology (ISCSITR-IJIT), 2(1), 1–4
S. Balasubramanian, AI-Driven Solutions for Sustainable Infrastructure Development and Management. International Journal of Artificial Intelligence in Engineering (IJAIE), 2(1), 2024, 1-11.
Rossi, Emanuele, et al. "Temporal Graph Networks for Deep Learning on Dynamic Graphs." arXiv preprint arXiv:2006.10637, 2020.
Mukesh, V. (2025). Architecting intelligent systems with integration technologies to enable seamless automatio in distributed cloud environments. International Journal of Advanced Research in Cloud Computing (IJARCC), 6(1),5-10.
Xu, Da, et al. "Inductive Representation Learning on Temporal Graphs." arXiv preprint arXiv:2002.07962, 2020.
Pareja, Arturo, et al. "EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs." Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, 2020, pp. 5363–5370.
Vinay, S. B. (2024). AI-Driven Patent Mining: Unveiling Innovation Patterns through Automated Knowledge Extraction. International Journal of Super AI (IJSAI), 1(1), 111.
Praba, P., & Balasubramanian, S. (2010). Shared bandwidth reservation of backup paths of multiple LSP against link and node failures. International Journal of Computer Engineering and Technology (IJCET), 1(1), 92–102.
Mukesh, V. (2024). A Comprehensive Review of Advanced Machine Learning Techniques for Enhancing Cybersecurity in Blockchain Networks. ISCSITR-International Journal of Artificial Intelligence, 5(1), 1–6.
Wu, Zonghan, et al. "A Comprehensive Survey on Graph Neural Networks." IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 1, 2020, pp. 4–24.
Kazemi, Seyed Mehran, et al. "Representation Learning for Dynamic Graphs: A Survey." Journal of Machine Learning Research, vol. 21, no. 70, 2020, pp. 1–73.
Ma, Jing, et al. "Detecting Rumors from Microblogs with Recurrent Neural Networks." Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), 2019, pp. 3818–3824.
S.B. Vinay, "Data Scientist Competencies and Skill Assessment: A Comprehensive Framework," International Journal of Data Scientist (IJDST), vol. 1, issue 1, pp. 1-11, 2024.
Mukesh, V., Joel, D., Balaji, V. M., Tamilpriyan, R., & Yogesh Pandian, S. (2024). Data management and creation of routes for automated vehicles in smart city. International Journal of Computer Engineering and Technology (IJCET), 15(36), 2119–2150. doi: https://doi.org/10.5281/zenodo.14993009
Nguyen, Giang Hoang, et al. "Continuous-Time Dynamic Network Embeddings." Proceedings of the 27th International Conference on World Wide Web (WWW), 2018, pp. 969–976.
Trivedi, Rakshit, et al. "DyRep: Learning Representations over Dynamic Graphs." International Conference on Learning Representations (ICLR), 2019.
Pradip Kumar Krishnadevarajan, S. Balasubramanian and N. Kannan. Stratification: A Key Tool to Drive Business Focus and Complexity Management International Journal of Management, 6(7), 2015, pp. 86-93.
Sankar, Aravind, et al. "Dynamic Graph Representation Learning via Self-Attention Networks." Proceedings of the Thirteenth ACM International Conference on Web Search and Data Mining (WSDM), 2020, pp. 567–575.
S. B. Vinay, Application of Artificial Intelligence (AI) In Publishing Industry in India, International Journal of Computer Engineering and Technology (IJCET) 14(1), 2023, pp. 7-12.DOI: https://doi.org/10.17605/OSF.IO/4D5M7
Zhou, Chuan, et al. "Scalable Graph Embedding for Asynchronous Dynamic Networks." Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
S. Balasubramanian, AI-Powered Trademark Registration Systems: Streamlining Processes and Improving Accuracy, International Journal of Intellectual Property Rights (IJIPR), 14(1), 2024, 1-7.
Kumar, Srijan, Xikun Zhang, and Jure Leskovec. "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 1269–1278.
