Abstract
Link prediction is an attractive and sought-after topic for research in network analysis. Link prediction has a great importance in various social networks like Facebook, Twitter, LinkedIn, Koo by recommending future associations in these networks. In addition to this, it has many applications in other fields like eCommerce, Education, Biology, and Security. In this paper, we consider some recent and baseline similarity-based approaches from the existing literature to gauge their performance. For performance evaluation, we use the metrics: AUC, precision, recall, accuracy, and f1 score. The experimental results illustrate the performance of link prediction algorithms on various datasets. This study has potential practical applications for designing a novel algorithm for link prediction in various networks.
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