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Applications

  • Ali Mohammad SaghiriEmail author
  • M. Daliri Khomami
  • Mohammad Reza Meybodi
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

All of the intelligent models of random walk proposed in Chap.  2 are domain independent. Therefore, these models can be applied in a wide variety of problems. As it was previously discussed, all of the proposed models can be used to design prediction models based on random walk algorithms. This characteristic can be used to design problem-solving methods in large-scale systems such as peer-to-peer networks and social networks. Therefore, the proposed models will be used to solve two problems in peer-to-peer networks and also one problem in social networks. In peer-to-peer networks, we focus on designing intelligent search algorithms which lead to find a specific object in the network. In social networks, we focus on designing an intelligent mechanism for finding a set of nodes called PIDS which leads to solve influence maximization problem. In the rest of this section, we give the required information about the selected problems, and then several solutions based on intelligent models of random walk are studied.

Keywords

Intelligent models of random walk Prediction models Peer-to-Peer networks Intelligent search algorithms Social networks Influence maximization 

References

  1. 1.
    Kwok YK (2011) Peer-to-peer computing: applications, architecture, protocols, and challenges. CRC Press, United StatesCrossRefGoogle Scholar
  2. 2.
    Tschorsch F, Scheuermann B (2016) Bitcoin and beyond: a technical survey on decentralized digital currencies. IEEE Commun Surv Tutorials 18:2084–2123CrossRefGoogle Scholar
  3. 3.
    Saghiri AM, Meybodi MR (2016) An approach for designing cognitive engines in cognitive peer-to-peer networks. J Netw Comput Appl 70:17–40.  https://doi.org/10.1016/j.jnca.2016.05.012CrossRefGoogle Scholar
  4. 4.
    Chawathe Y, Ratnasamy S, Breslau L, Lanham N, Shenker S (2003) Making gnutella-like p2p systems scalable. In: Proceedings of the conference on applications, technologies, architectures, and protocols for computer communications. ACM, Karlsruhe, Germany, pp 407–418Google Scholar
  5. 5.
    Clarke I, Sandberg O, Wiley B, Hong T (2001) Freenet: a distributed anonymous information storage and retrieval system. Designing privacy enhancing technologies. Springer, Berkeley, USA, pp 46–66CrossRefGoogle Scholar
  6. 6.
    Stoica I, Morris R, Karger D, Kaashoek MF, Balakrishnan H (2001) Chord: a scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM Comput Commun Rev 31:149–160CrossRefGoogle Scholar
  7. 7.
    Ratnasamy S, Francis P, Handley M, Karp R, Shenker S (2001) A scalable content-addressable network. ACM SIGCOMM Comput Commun Rev 31:161–172CrossRefGoogle Scholar
  8. 8.
    Gkantsidis C, Mihail M, Saberi A (2006) Random walks in peer-to-peer networks: algorithms and evaluation. Perform Eval 63:241–263CrossRefGoogle Scholar
  9. 9.
    Ghorbani M, Meybodi MR, Saghiri AM (2013) A new version of k-random walks algorithm in peer-to-peer networks utilizing learning automata. 5th Conference on information and knowledge technology. IEEE Computer Society, Shiraz, Iran, pp 1–6Google Scholar
  10. 10.
    Ghorbani M, Meybodi MR, Saghiri AM (2013) A novel self-adaptive search algorithm for unstructured peer-to-peer networks utilizing learning automata. 3rd joint conference of AI & Robotics and 5th RoboCup Iran Open International Symposium. IEEE, Qazvin, Iran, pp 1–6Google Scholar
  11. 11.
    Li X, Wu J (2006) Improve searching by reinforcement learning in unstructured P2Ps. In: 26th IEEE international conference on distributed computing systems workshops. IEEE, PortugalGoogle Scholar
  12. 12.
  13. 13.
    Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117CrossRefGoogle Scholar
  14. 14.
    Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49:291–307CrossRefGoogle Scholar
  15. 15.
    Quick review of graph mining with R. In: Quick review of graph mining with R. http://www.lumenai.fr/blog/quick-review-of-graph-mining-with-r. Accessed 29 Sept 2018
  16. 16.
    Wang F, Du H, Camacho E, Xu K, Lee W, Shi Y, Shan S (2011) On positive influence dominating sets in social networks. Theoret Comput Sci 412:265–269MathSciNetCrossRefGoogle Scholar
  17. 17.
    Baumgart I, Heep B, Krause S (2007) OverSim: a flexible overlay network simulation framework. IEEE global internet symposium. IEEE Computer Society, Anchorage, USA, pp 79–84Google Scholar
  18. 18.
    Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, USA, pp 137–146Google Scholar
  19. 19.
    Brin S, Page L (1998) The anatomy of a large-scale hypertextual Web search engine. Comput Netw ISDN Syst 30:107–117CrossRefGoogle Scholar
  20. 20.
    Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM (JACM) 46:604–632MathSciNetCrossRefGoogle Scholar
  21. 21.
    Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, USA, pp 420–429Google Scholar
  22. 22.
    Goyal A, Lu W, Lakshmanan LV (2011) Celf ++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on World wide web. ACM, India, pp 47–48Google Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ali Mohammad Saghiri
    • 1
    Email author
  • M. Daliri Khomami
    • 1
  • Mohammad Reza Meybodi
    • 1
  1. 1.Amirkabir University of TechnologyTehranIran

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