Abstract
In complex systems with interactive elements, link prediction plays an important role. It forecasts future or missing associations among entities of a complex system using the current network information. Predicting future or missing links has a wide variety of application areas in several domains like social, criminal, biological, and academic networks. This paper presents a novel method for finding missing or future links that uses the concepts of proximity between the vertices of a network and the number of associations of the common neighbors. We test the performance of our method on four real networks of varying sizes. We tested it against six state-of-the-art similarity-based algorithmss. The outcomes of the experimental evaluation demonstrate that the proposed strategy outperforms others. It remarkably improves the prediction accuracy in considerable computing time.
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Rai, A.K., Yadav, R.K., Tripathi, S.P., Singh, P., Sharma, A. (2024). A Novel Similarity-Based Method for Link Prediction in Complex Networks. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14532. Springer, Cham. https://doi.org/10.1007/978-3-031-53830-8_32
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