Development of Katz Similarity Index to Improve Link Prediction in Social Networks Based on Reliable Routes

Musa Mojarad¹ , Omid Ranjbar Dehghan² , Amin Ranjbar Dehghan³ , Alireza Salar⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.3 , pp.16-21, Jun-2021

Online published on Jun 30, 2021

Copyright © Musa Mojarad, Omid Ranjbar Dehghan, Amin Ranjbar Dehghan, Alireza Salar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Online social networks are a new generation of databases that are in the spotlight of Internet users these days. In fact, social networks have a multi-layered architecture. This means that there can be links between users on several different networks. Predicting links in social networks is one of the most important activities in social media analysis. In this article, link prediction is extracted by extracting different features between users in social networks through multiple layers. Most real-world social networks promote communication in multiple layers (i.e., multiplex social networks). Here, problem of link prediction on several networks, namely Foursquare and Twitter, is examined. Here, only users who have a shared account on both networks are considered. The link prediction process for the Foursquare social network is based on connection information from both layers. By extracting structural features between users and using reliable routes, a new method according to Katz similarity index is suggested to compute final similarity between users. Experiments show that the suggested algorithm can successfully predict links for the Foursquare social network through multilayer information.

Key-Words / Index Term :
Social Networks; Link Prediction; Multiplex; Reliable Routes; Katz Index

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