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Load balanced diffusive capture process on homophilic scale-free networks

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  • Telcs, András
  • Csernai, Márton
  • Gulyás, András
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    Abstract

    Diffusive capture processes are known to be an effective method for information search on complex networks. The biased N lions–lamb model provides quick search time by attracting random walkers to high degree nodes, where most capture events take place. The price of the efficiency is extreme traffic concentration on top hubs. We propose traffic load balancing provided by type specific biased random walks. For that we introduce a multi-type scale-free graph generation model, which embeds homophily structure into the network by utilizing type dependent random walks. We show analytically and with simulations that by augmenting the biased random walk method with a simple type homophily rule, we can alleviate the traffic concentration on high degree nodes by spreading the load proportionally between hubs with different types of our generated multi-type scale-free topologies.

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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 392 (2013)
    Issue (Month): 3 ()
    Pages: 510-519

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    Handle: RePEc:eee:phsmap:v:392:y:2013:i:3:p:510-519

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Random walk; Diffusive capture process; Scale-free network; Community structure; Information search; Load balancing;

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    1. Josep M. Pujol & Andreas Flache & Jordi Delgado & Ramon Sang�esa, 2005. "How Can Social Networks Ever Become Complex? Modelling the Emergence of Complex Networks from Local Social Exchanges," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 12.
    2. Saramäki, Jari & Kaski, Kimmo, 2004. "Scale-free networks generated by random walkers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 341(C), pages 80-86.
    3. Lee, Sungmin & Yook, Soon-Hyung & Kim, Yup, 2007. "Diffusive capture processes for information search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 743-749.
    4. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    5. Lynne Hamill & Nigel Gilbert, 2009. "Social Circles: A Simple Structure for Agent-Based Social Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 3.
    6. Firat, Aykut & Chatterjee, Sangit & Yilmaz, Mustafa, 2007. "Genetic clustering of social networks using random walks," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6285-6294, August.
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