Integrating neural network-driven customization, scalability, and cloud computing for enhanced accuracy and responsiveness for social network modelling

Social network models often struggle to capture and adapt to these changing dynamics, leading to limitations in accuracy, scalability, and customization. This research introduces a novel approach to enhancing social network modelling through the integration of advanced techniques such as neural network-driven customization and scalability. Leveraging Multi-Layer Perceptron (MLP) architectures accommodate the complexity and dynamics inherent in modern social networks. The methodology incorporates Wi-Fi Aware (NAN) technology, facilitating efficient neighbor awareness networking to capture real-time interactions and network topologies. Graph Neural Networks (GNN) is employed to handle the intricate structure of social graphs, optimizing connectivity and information propagation within the network. The power of cloud computing platforms such as Microsoft Azure and serverless frameworks like AWS Lambda is harnessed to ensure scalability and flexibility in the model. Tokenization techniques are utilized for efficient data representation and processing, while Feature Extraction Modules enable the extraction of meaningful features from raw social network data. To address the challenges of decision-making and optimization in dynamic social environments, reinforcement learning algorithms such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Model-Agnostic Meta-Learning (MAML) are integrated. These algorithms enable adaptive learning and decision-making, enhancing the model's responsiveness to changing network conditions. Through extensive experimentation and evaluation, the efficacy of the approach in achieving significant improvements in social network modelling such as accuracy, scalability, and customization is demonstrated. The overall result indicates that the average accuracy across all techniques demonstrates notable variations, with the highest average accuracy exceeding 84.8% and the lowest falling below 78.4%.