Link Prediction in Complex Hyper-Networks Leveraging HyperCentrality

In complex networks, predicting the formation of new connections, or links, within complex networks has been a central challenge, traditionally addressed using graph-based models. These models, however, are limited in their ability to capture higher-order interactions that exist in many real-world networks, such as social, biological, and technological systems. To account for these multi-node interactions, hyper-networks have emerged as a more flexible framework, where hyperedges can connect multiple nodes simultaneously. Traditional link prediction methods often treat all common neighbors equally, overlooking the fact that not all nodes contribute uniformly to the formation of future links. Each node within a network holds a distinct level of importance, which can influence the likelihood of link formation among its neighbors. To address this, we introduce a link prediction approach leveraging hypercentrality measures adapted from traditional centrality metrics such as degree, clustering coefficient, betweenness, and closeness to capture node significance and improve link prediction in hyper-networks. We propose the Link Prediction Based on HyperCentrality in hyper-networks (LPHC) model, which enhances traditional common neighbor and jaccard coefficient of hyper-network frameworks by incorporating centrality scores to account for node importance. Our approach is evaluated across multiple real-world hyper-networks datasets, demonstrating its superiority over traditional link prediction methods. The results show that link prediction in hypercentrality-based models, particularly those utilizing hyperdegree and hyperclustering coefficients for common neighbor and jaccard coefficent approaches in hyper-networks, consistently outperform existing methods in terms of both F1-score and Area Under the Precision-Recall Curve (AUPR), offering a more precise understanding of potential link formations in hyper-networks. The proposed LPHC model consistently outperforms the existing HCN and HJC models across all datasets, achieving an overall improvement of 69% compared to HCN and 68% compared to HJC.