Abstract
Link prediction is an emerging and fast-growing applied research area. In a network, it is possible to predict the next link which is going to be formed. The usefulness of link prediction modeling has been proved in several fields and applications, such as biomedicine, recommending systems, and social media. In this short paper, we discuss the potential of Variational Graph Autoencoder, by comparing the results so obtained against those by some similarity-based methods, such as Adamic-Adar, Jaccard coefficient, and Preferential Attachment.
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Roy, S.S., Ranjan, A., Tomasiello, S. (2021). Link Prediction in Social Networks by Variational Graph Autoencoder and Similarity-Based Methods: A Brief Comparative Analysis. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_30
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