Adaptive Deep Fuzzy Neural Network With Multi-Headed Self-Attention for Improving Performance of Social Network Representation Learning and Link Prediction

Abstract

Link prediction has long been a fundamental task in graph data analysis, aimed at identifying potential or missing connections between nodes. This task is particularly important for understanding social dynamics and improving the robustness of networks. However, existing graph learning methods often struggle with modeling complex structures, especially in social networks where data is noisy, uncertain, as well as multi-faceted. Most traditional graph embedding architectures are limited in their ability to handle noise and effectively represent multi-view information. Likewise, there are several graph neural networks (GNNs) face challenges in integrating diverse structural perspectives and managing uncertainty, which leads to subpar performance in link prediction tasks. To overcome these challenges, we propose a novel model called AFGRL which is an attention-driven fuzzy graph representation learning. Our proposed AFGRL combines multiple types of GNNs for multi-view embedding with a multi-headed attention-enhanced fuzzy neural network. This design enables our AFGRL model to better learn richer, as well as more expressive graph representations while effectively managing uncertainty and noise. The attention mechanism integrated into our AFGRL model allows it to focus on various structural aspects of the graph—while the fuzzy logic component captures ambiguity inherent in social network data. Our model is particularly tailored for online social networks—where user relationships are dynamic and characterized by varying degrees of trust and influence. We evaluate AFGRL on several real-world and benchmark datasets, demonstrating its superior performance in link prediction compared to state-of-the-art baselines. The results confirm that our AFGRL model not only enhances predictive accuracy but also provides robust and meaningful structural representations; as a result, highlighting the value of integrating attention and fuzzy logic into graph learning frameworks.