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Modeling memetics using edge diversity

Modeling memetics using edge diversity The diffusion of an idea significantly differs from the diffusion of a disease because of the interplay of the complex sociological and behavioral factors in the former. Hence, the conventional epidemiological models fail to capture the heterogeneity of social networks and the complexity of information diffusion. Standard information diffusion models depend heavily on the micro-level parameters of the network like edge weights and implicit vulnerabilities of nodes towards information. Such parameters are rarely available because of the absence of large amounts of information diffusion data. Hence, modeling information diffusion remains a challenging research problem. In this paper, we utilize the peculiar structure of the real-world social networks to derive useful insights into the micro-level parameters. We propose an artificial framework mimicking the real-world information diffusion. The framework includes (1) a synthetic network which structurally resembles a real-world social network and (2) a meme spreading model based on the penta-level classification of edges in the network. The experimental results prove that the synthetic network combined with the proposed spreading model is able to simulate a real-world meme diffusion. The framework is validated with the help of the diffusion data of the Higgs boson meme on Twitter and the datasets of several popular real-world social networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Austria, part of Springer Nature
Subject
Computer Science; Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Sciences, Humanities, Law; Methodology of the Social Sciences
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-018-0546-6
Publisher site
See Article on Publisher Site

Abstract

The diffusion of an idea significantly differs from the diffusion of a disease because of the interplay of the complex sociological and behavioral factors in the former. Hence, the conventional epidemiological models fail to capture the heterogeneity of social networks and the complexity of information diffusion. Standard information diffusion models depend heavily on the micro-level parameters of the network like edge weights and implicit vulnerabilities of nodes towards information. Such parameters are rarely available because of the absence of large amounts of information diffusion data. Hence, modeling information diffusion remains a challenging research problem. In this paper, we utilize the peculiar structure of the real-world social networks to derive useful insights into the micro-level parameters. We propose an artificial framework mimicking the real-world information diffusion. The framework includes (1) a synthetic network which structurally resembles a real-world social network and (2) a meme spreading model based on the penta-level classification of edges in the network. The experimental results prove that the synthetic network combined with the proposed spreading model is able to simulate a real-world meme diffusion. The framework is validated with the help of the diffusion data of the Higgs boson meme on Twitter and the datasets of several popular real-world social networks.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Dec 3, 2018

References