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Modeling memetics using edge diversity

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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.

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Notes

  1. This sentence has just been used as an example. However, studies indicate that the most wealthy and affluent people tend to be the most influential in our societies (Easley and Kleinberg 2010).

  2. The reason is described in Sect. 5.

  3. Higgs boson is one of the most elementary elusive particles in modern physics. A meme on Twitter is considered to be a Higgs boson meme if it contains at least one of these keywords or tags: LHC, CERN, boson, Higgs

  4. Homophily is the name given to the tendency of similar people becoming friends with each other. This leads to more number of ties between like-minded people and hence leads to the formation of communities in the network. Social reinforcement is the phenomenon by which multiple exposures of an information to a person lead to him adopting it. Social reinforcement and homophily tend to block the information inside one community

  5. In the case of random network, even though the declared \(10\%\) core nodes have a high probability of infecting their neighbors, the connections between these nodes are not dense enough to result in an overshoot in the number of infected nodes. Therefore, an absence of a distinct core-periphery structure in such networks makes them invalid for our framework.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India

    Yayati Gupta, S. R. S. Iyengar & Akrati Saxena

  2. Department of Computer Science, University of Minnesota, Twin Cities, Minneapolis, USA

    Debarati Das

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  1. Yayati Gupta
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  2. S. R. S. Iyengar
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  3. Akrati Saxena
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  4. Debarati Das
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Gupta, Y., Iyengar, S.R.S., Saxena, A. et al. Modeling memetics using edge diversity. Soc. Netw. Anal. Min. 9, 2 (2019). https://doi.org/10.1007/s13278-018-0546-6

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