An approach for predicting missing links in social network using node attribute and path information

Ankita Singh () and Nanhay Singh
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Ankita Singh: University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University
Nanhay Singh: NSUT East Campus (Formerly AIACTR)

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 2, No 30, 944-956

Abstract: Abstract In social networks, link prediction is the task to identify links in future. Many existing link prediction techniques used similarity scores to predict links. An essential concern in the link prediction problem is identifying missing links between the nodes when there are no common neighbors between the nodes. Considering this, a new algorithm proposed, namely Similarity-based Algorithm using Degree and Common Neighbour (SADCN) which includes a node's degree in the shortest path and common neighbor. For experiment evaluation, three datasets are used to test our method performance against some standard similarity index and the recently proposed algorithms for link prediction, which depicts that our approach achieved comparable AUC values to those that consider common neighbors and it gives better AUC for those links, where no mutual neighbour between the two nodes exists. Finally, we create feature vectors and use XGB classifiers for predicting links. It shows that our proposed algorithm can improve the F-measure and accuracy in a feature based link prediction model.

Keywords: Social network; Link; Prediction; Similarity; Topological (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01371-w

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