DEVELOPMENT OF LINK PREDICTION MODEL IN SOCIAL NETWORK

Abstract

Link prediction in social networks plays a crucial role in understanding network evolution, identifying potential interactions, and supporting applications such as rec- ommendation systems, community analysis, and the discovery of biological networks. The fundamental problem of link prediction is to estimate the likelihood of future or missing connections between pairs of nodes based on existing network information, structural patterns, node attributes, and temporal evolution. However, real-world net- works are highly complex, sparse, dynamic, and heterogeneous, making traditional similarity-based and shallow learning approaches insufficient to capture deep struc- tural semantics and evolving behavioral patterns. In this thesis, we introduce a robust and adaptive approach to link prediction in social networks. The present study integrates traditional similarity-based techniques with advanced deep music recommendations, among effective similarity scores ex- isting methods for list structure- and attribute-aware information, a single similarity index, or paths from performance and reliability of the proposed methodology. The first model, GSVAELP, introduces a hybrid GraphSAGE-VAE model that lever- ages local neighborhood aggregation with probabilistic latent-space embedding, suc- cessfully capturing both structural dependencies and latent relational patterns. This laid the foundation for robust structure-and-attribute-aware link prediction. The second study, MetaLP-DGI, introduced centrality-aware Deep Graph Infomax with meta-learning, enhancing embedding quality by incorporating influential node characteristics while improving generalization across heterogeneous networks. The third model, Hybrid Graph Embedding and Ensemble Learning, demonstrated that combining multiple embeddings with ensemble classifiers significantly improves predictive consistency and reduces model bias. vi Further, the fourth model enhancement is achieved through MetaLP-DGI, which utilizes Deep Graph Infomax (DGI) embeddings integrated with a centrality-aware transition matrix to capture both global and local structural dependencies. The meta- learning component in MetaLP-DGI optimizes the learning process across heteroge- neous datasets, improving robustness and adaptability. Complementing these in-depth approaches. The fifth study, Link Prediction in Social Networks: A Hybrid Approach with Graph Embedding and Ensemble Learning, combines structure- and attribute- based embeddings with ensemble classifiers, such as CatBoost and Random Forest, to deliver high-accuracy predictions in social network scenarios. Finally, the last study, UnifiedAttri2Vec–LSTM constructs a unified embedding by integrating multiple em- bedding algorithms through Attri2Vec and leverages LSTM to model temporal and structural dependencies simultaneously. Overall, this thesis contributes a comprehensive exploration of hybrid, generative, and meta-learning-based frameworks for link prediction, establishing a strong founda- tion for adaptive and scalable graph analytics. The progressive integration of centrality, attention, temporal evolution, and ensemble learning provides a unified roadmap for advancing intelligent link prediction in complex and dynamic networked systems.