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Modeling Memetics Using Edge Diversity

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Complex Networks VII

Part of the book series: Studies in Computational Intelligence ((SCI,volume 644))

Abstract

The study of meme propagation and the prediction of meme trajectory are emerging areas of interest in the field of complex networks research. In addition to the properties of the meme itself, the structural properties of the underlying network decides the speed and the trajectory of the propagating meme. In this paper, we provide an artificial framework for studying the meme propagation patterns. Firstly, the framework includes a synthetic network which simulates a real world network and acts as a testbed for meme simulation. Secondly, we propose a meme spreading model based on the diversity of edges in the network. Through the experiments conducted, we show that the generated synthetic network combined with the proposed spreading model is able to simulate a real world meme spread. Our proposed model is validated by the propagation of the Higgs boson meme on Twitter as well as many real world social networks.

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Notes

  1. 1.

    The work shows that scale free networks possess a core-periphery structure. They define cp-centralisation value which is a measure of the degree to which a network contains a core-periphery structure. According to this study, the average cp-centralisation value for 1000 instances of scale free networks with 100 nodes and average degree 4 is 0.668.

  2. 2.

    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 leads to him adopting it. Social reinforcement and homophily tend to block the information inside one community.

  3. 3.

    In the case of random network, even though the declared 10 % core nodes have a high probability of infecting their neighbours, the connections between the core nodes are not dense enough to result in an overshoot in the number of infected nodes.

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Acknowledgments

S.R.S. Iyengar was partially supported by the ISIRD grant(Ref. No. IITRPR/Acad./359) from IIT Ropar. Further, we express our gratitude to the Indian Academy of Sciences,Bangalore for providing us with partial funding to carry out this research.

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Authors and Affiliations

  1. Indian Institute of Technology Ropar, Rupnagar, India

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

  2. PES Institute of Technology, Bangalore, India

    Debarati Das

Authors
  1. Yayati Gupta
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  2. Akrati Saxena
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  3. Debarati Das
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  4. S. R. S. Iyengar
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Corresponding author

Correspondence to Yayati Gupta .

Editor information

Editors and Affiliations

  1. LE2I , UFR Sciences et Techniques, Université de Bourgogne, Dijon, France

    Hocine Cherifi

  2. New York University, Center for Data Science, New York, USA

    Bruno Gonçalves

  3. BioComplex Laboratory, Computer Sciences, Florida Institute of Technology, Melbourne, FL, Florida, USA

    Ronaldo Menezes

  4. 111 Dana, Physics Department, Northeastern University, Boston, Massachusetts, USA

    Roberta Sinatra

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Gupta, Y., Saxena, A., Das, D., Iyengar, S.R.S. (2016). Modeling Memetics Using Edge Diversity. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-30569-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30568-4

  • Online ISBN: 978-3-319-30569-1

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