A Novel Hybrid Approach for Similarity-Based Link Prediction in Complex Networks

Abstract

Link prediction in complex networks is the challenging task of predicting missing or future connections between nodes. Complex networks, such as social networks, biological networks, and online recommendation systems, are often incomplete, with unknown or unestablished relationships between nodes. Link prediction algorithms fill in these missing links by leveraging the existing network structure and properties. By analyzing network patterns, connectivity, and characteristics, these algorithms can predict the likelihood of future connections, enabling applications such as recommender systems, network analysis, and understanding the dynamics of complex systems. Link prediction is a crucial task for providing relevant recommendations in e-commerce recommender systems. However, it is challenging due to the sparsity of user-item interaction data and the dynamic nature of user preferences. In this paper, we propose a novel hybrid link prediction algorithm that combines the Jaccard Coefficient Similarity Index, Adamic Adar Index, and MapSim Similarity based Index methods. Our algorithm leverages the complementary strengths of each individual method to improve the overall prediction accuracy. The Jaccard Coefficient Similarity Index measures the similarity between two users or items based on the number of shared items or users. The Adamic Adar Index considers the common neighbors between two users or items to predict the link probability. The MapSim Similarity based Index method incorporates the geographic location of users and items to predict the link probability. We evaluate our proposed hybrid algorithm on two real-world e-commerce datasets, and the results show that it outperforms several state-of-the-art link prediction algorithms in terms of accuracy and precision.