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An information propagation model for social networks based on continuous-time quantum walk

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Abstract

Existing social network simulation models exhibit several limitations, including extensive iteration requirements and multiple control parameters. In this study, an information propagation model based on continuous-time quantum walk (CTQW-IPM) is introduced to rank crucial individuals in undirected social networks. In the proposed CTQW-IPM, arbitrary individuals (or groups) can be specified as initial diffusion dynamic elements through preset probability amplitudes. Information diffusion on a global reachable path is then simulated by an evolution operator, as individual degrees of cruciality are estimated from probability distributions acquired from quantum observations. CTQW-IPM does not require iterations, due to the non-randomness of CTQW, and does not include extensive computations as complex cascade diffusion processes are replaced by evolution operators. Experimental comparisons of CTQW-IPM and several conventional models showed their ranking of crucial individuals exhibited a strong correlation, with nearly every individual in the social network assigned a unique measured value based on the rate of distinguishability. CTQW-IPM also outperformed other algorithms in influence maximization problems, as measured by the resulting spread size.

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References

  1. Kabir KMA, Kuga K, Tanimoto J (2019) Analysis of SIR epidemic model with information spreading of awareness. Chaos Solitons Fractals 119:118–125

    Article  MathSciNet  Google Scholar 

  2. Zhang J, Yu PS (2019) Broad learning through fusions—an application on social networks. Springer, New York

    Book  Google Scholar 

  3. Chen W, Lakshmanan LVS, Castillo C (2013) Information and influence propagation in social networks. Morgan and Claypool Publishers, New York

    Google Scholar 

  4. He Q, Sun L, Wang X, Wang Z, Ma L (2021) Positive opinion maximization in signed social networks. Inf Sci 558(2):34–49

    Article  MathSciNet  Google Scholar 

  5. Chen L, Zhang Y, Chen Y, Li B, Liu W (2021) Negative influence blocking maximization with uncertain sources under the independent cascade model. Inf Sci 564:343–367

    Article  MathSciNet  Google Scholar 

  6. Zarezade A, Khodadadi A, Farajtabar M, Rabiee HR, Zha H (2017) Correlated cascades: compete or cooperate. In: Proceedings of the 31th AAAI conference on artificial intelligence, vol 31. AAAI Press, San Francisco, pp 238–244

  7. Khadangi E, Bagheri A, Zarean A (2018) Empirical analysis of structural properties, macroscopic and microscopic evolution of various facebook activity networks. Qual Quant 52(1):249–275

    Article  Google Scholar 

  8. Zhan C, Wu F, Huang Z, Jiang W, Zhang Q (2020) Analysis of collective action propagation with multiple recurrences. Neural Comput Appl 32(17):13491–13504

    Article  Google Scholar 

  9. Zhan C, Li B, Zhong X, Min H, Wu Z (2020) A model for collective behaviour propagation: a case study of video game industry. Neural Comput Appl 32(9):4507–4517

    Article  Google Scholar 

  10. Zhan C, Chi KT, Small M (2016) A general stochastic model for studying time evolution of transition networks. Physica A 464:198–210

    Article  MathSciNet  Google Scholar 

  11. Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proc Roy Soc Lond Ser A Contain Pap Math Phys Charact 115(772):700–721

    MATH  Google Scholar 

  12. Kempe D, Kleinberg JM, 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, Washington, DC, pp 137–146

  13. Herbert HW (2000) The mathematics of infectious diseases. SIAM Rev 42(4):599–653

    Article  MathSciNet  Google Scholar 

  14. Ben-Naim E, Krapivsky PL (2004) Size of outbreaks near the epidemic threshold. Phys Rev E 69:050901

    Article  Google Scholar 

  15. Ma H, Yang H, Lyu MR, King I (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, Napa Valley, pp 233–242

  16. AlSuwaidan L, Ykhlef M (2017) A novel information diffusion model for online social networks. In: Proceedings of the 19th international conference on information integration and web-based applications & services. ACM, Salzburg, pp 116–120

  17. Pierre P, Jennifer H, Zheng W, Michal V (2020) Budgeted online influence maximization. In: Proceedings of the 37th international conference on machine learning, volume 119 of proceedings of machine learning research. PMLR, Vienna, pp 7620–7631

  18. Bourigault S, Lamprier S, Gallinari P (2016) Representation learning for information diffusion through social networks: an embedded cascade model. In: Proceedings of the ninth ACM international conference on web search and data mining. ACM, San Francisco, pp 573–582

  19. Panagopoulos G, Malliaros F, Vazirgiannis M (2020) Multi-task learning for influence estimation and maximization. IEEE Trans Knowl Data Eng 1–13

  20. Chakraborty S, Novo L, Roland J (2020) Finding a marked node on any graph via continuous-time quantum walks. Phys Rev A 102(2):022227

    Article  MathSciNet  Google Scholar 

  21. Venegas-Andraca SE (2012) Quantum walks: a comprehensive review. Quantum Inf Process 11(5):1015–1106

    Article  MathSciNet  Google Scholar 

  22. Gong M, Wang S, Zha C, Chen M, Huang H, Wu Y, Zhu Q, Zhao Y, Li S, Guo S, Qian H, Ye Y, Chen F, Ying C, Yu J, Fan D, Wu D, Su H, Deng H, Rong H, Zhang K, Cao S, Lin J, Xu Y, Sun L, Guo C, Li N, Liang F, Bastidas VM, Nemoto K, Munro WJ, Huo Y, Lu C, Peng C, Zhu X, Pan J (2021) Quantum walks on a programmable two-dimensional 62-qubit superconducting processor. Science 372(6545):948–952

    Article  Google Scholar 

  23. Harrow AW, Hassidim A, Lloyd S (2009) Quantum algorithm for linear systems of equations. Phys Rev Lett 103(15):150502

    Article  MathSciNet  Google Scholar 

  24. Berry SD, Wang JB (2011) Two-particle quantum walks: entanglement and graph isomorphism testing. Phys Rev A 83(4):042317

    Article  Google Scholar 

  25. Loke T, Tang J, Rodriguez J, Small M, Wang J (2017) Comparing classical and quantum PageRanks. Quantum Inf Process 16(1):1–22

    Article  Google Scholar 

  26. Mukai K, Hatano N (2020) Discrete-time quantum walk on complex networks for community detection. Phys Rev Res 2(17):023378

    Article  Google Scholar 

  27. Childs AM (2010) On the relationship between continuous-and discrete-time quantum walk. Commun Math Phys 294(2):581–603

    Article  MathSciNet  Google Scholar 

  28. Izaac JA, Zhan X, Bian Z, Wang K, Wang J (2017) Centrality measure based on continuous-time quantum walks and experimental realization. Phys Rev A 95(3):032318

    Article  Google Scholar 

  29. Rossi RA, Ahmed NK (2015) The network data repository with interactive graph analytics and visualization. In: Proceedings of the 29th AAAI conference on artificial intelligence, volume 29. AAAI Press, Austin, pp 4292–4293

  30. Han Z, Chen Y, Li M (2016) An efficient node influence metric based on triangle in complex networks. J Chin Phys 65(16):289–300

    Google Scholar 

  31. Sergey B, Lawrence P (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw 30(1–7):107–117

    Google Scholar 

  32. Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893

    Article  Google Scholar 

  33. Paparo GD, Mller M, Comellas F, Martin-Delgado MA (2013) Quantum google in a complex network. Sci Rep 3(1):1–16

    Article  Google Scholar 

  34. Newman ME (2005) A measure of betweenness centrality based on random walks. Soc Netw 27(1):39–54

    Article  Google Scholar 

  35. Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD. ACM, New York, pp 199–208

  36. Qiu L, Gu C, Zhang S, Tian X, Zhang M (2020) TSIM: a two-stage selection algorithm for influence maximization in social networks. IEEE Access 8:12084–12095

    Article  Google Scholar 

  37. Daud NN, Ab Hamid SH, Saadoon M, Sahran F, Anuar NB (2020) Applications of link prediction in social networks: a review. J Netw Comput Appl 166:102716

    Article  Google Scholar 

Download references

Funding

This work is supported by the Jilin Provincial Department of Science and Technology, China (No. 20210201075GX).

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Authors and Affiliations

  1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China

    Fei Yan & Wen Liang

  2. Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan

    Kaoru Hirota

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  1. Fei Yan
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  2. Wen Liang
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  3. Kaoru Hirota
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Correspondence to Fei Yan.

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Yan, F., Liang, W. & Hirota, K. An information propagation model for social networks based on continuous-time quantum walk. Neural Comput & Applic 34, 13455–13468 (2022). https://doi.org/10.1007/s00521-022-07168-7

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  • DOI: https://doi.org/10.1007/s00521-022-07168-7

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