Title: Prediction of missing links in social networks: feature integration with node neighbour

Authors: Anand Kumar Gupta; Neetu Sardana

Addresses: Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Sector-62, Noida, Uttar Pradesh, India ' Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Sector-62, Noida, Uttar Pradesh, India

Abstract: Link prediction techniques are used to identify the future network structure on the basis of existing connectivity pattern of the users. Most of the existing link prediction techniques employ varied similarity indices to predict new links in network. Some techniques use common neighbours while others use common shared profile information of the user for prediction. Typically existing link prediction techniques have only focused on one of these two data modalities: common neighbours or common attributes. Both of them play equally important role in the dynamics of the network. In this paper, we propose a feature integrated node neighbour (FINN) approach, an accurate algorithm for predicting links in network. FINN integrates Jaccard coefficient and Adamic Adar to predict link between nodes using their connections and features. We have evaluated FINN by implementing it over the real-time Facebook dataset collected from SNAP repository and validated the result through area under ROC curve.

Keywords: link prediction; social network; feature integration with node neighbour; FINN; Jaccard index; Adamic Adar index; cosine similarity; similarity indices.

DOI: 10.1504/IJWBC.2018.090917

International Journal of Web Based Communities, 2018 Vol.14 No.1, pp.38 - 53

Received: 22 Mar 2016
Accepted: 23 Mar 2017

Published online: 16 Mar 2018 *

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