Title: A supervised learning approach for link prediction in complex social networks

Authors: Upasana Sharma; Sunil Kumar Khatri; L.M. Patnaik

Addresses: Amity Institute of Information Technology, Amity University, Uttar Pradesh, India ' Amity Institute of Information Technology, Amity University, Uttar Pradesh, India ' NIAS, Indian Institute of Science, Campus, Bengaluru, India

Abstract: In the current scenario, social networking is being used for social and business purpose such as Facebook, Twitter, and LinkedIn. Social networking websites are attracting the focus of many researchers. New links are being created in every fraction of a second. The major challenge in link prediction domain is to predict the future link. The social networks require effective and precise technique to predict the future connections in the network system. The focus of this work is on supervised machine learning approach for link prediction in complex social networks. Many researchers have been worked on supervised approach by using only unweighted networks in the past. Our aim is to assign weight to each connection in the network where the weight represents the strength of the connection. This paper introduces a new approach using closed triangle concept to recommend the future links in social networks. Extensive experiments have been performed on real YouTube data set and the proposed technique performs well and improves the accuracy of the link predictor.

Keywords: link prediction; social networks; artificial neural network; supervised learning approach; learning algorithms.

DOI: 10.1504/IJAIP.2021.117607

International Journal of Advanced Intelligence Paradigms, 2021 Vol.20 No.1/2, pp.1 - 15

Received: 16 Dec 2016
Accepted: 25 Jul 2017
Published online: 16 Sep 2021 *

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