Abstract
Social media is significantly contributing to information sharing between humans. Social networking sites Facebook, Twitter and LinkedIn are popular in connecting people across the world. For understanding and studying the behavior, social networks are represented through graphs referred as sociographs in which nodes are users and links between nodes are relationships. Social networks are dynamic as millions of new nodes are added to the networks making it large and complex to study and analyze. Predicting links in social network is a known computation problem wherein future link between the nodes is to be predicted. As to predict the links similarity between nodes deduced by calculating the similarity measure such that higher similarity measure value inferences existence of new link between nodes. In this work we are focused to detect and recognize the future links between the nodes by exploiting the node neighbourhood property. The proposed computation techniques compute the probability contribution measure for having link between the nodes.
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Bhanodia, P.K., Khamparia, A., Prajapat, S., Pandey, B., Sethi, K.K. (2024). Probability Approximation Based Link Prediction Method for Online Social Network. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_47
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DOI: https://doi.org/10.1007/978-3-031-47508-5_47
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