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Measuring Node Similarity for the Collective Attention Flow Network

  • Manfu Ma
  • Zhangyun Gong
  • Yong LiEmail author
  • Huifang Li
  • Qiang Zhang
  • Xiaokang Zhang
  • Changqing Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)

Abstract

Quantifying the similarity of nodes in collective attention flow network has an important theoretical and practical value. In this paper, we defined the generation time Rt, the influence radius Sr and the representation Vs (Rt, Sr) of the nodes in the collective attention flow network based on the optimization of Spatial Preferred Attachment (SPA) model. NID algorithm, based on the influence distance Sd that was calculated by the spatial norm, to measure the similarity of the nodes in the collective attention flow network was proposed. Experiments show that our algorithm not only accurately quantify the similarity of nodes in the collective attention flow network, but has a higher universality.

Keywords

Spatial Preferred Attachment (SPA) Collective attention flow network Node similarity algorithm 

Notes

Acknowledgements

This paper was supported by the Natural Science Foundation of China (No. 71764025, 61863032, 61662070); the Research Project on Educational Science Planning of Gansu, China (Grant No. GS[2018]GHBBKZ021, GS[2018]GHBBKW007); the Scientific Research Foundation of the Higher Education Department of Gansu, China (Grant No. 2018A-001). Author contributions: Manfu Ma and Zhangyun Gong are co-first authors who jointly designed the research. Correspondence and requests for materials should be addressed to Yong Li.

References

  1. 1.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998)CrossRefGoogle Scholar
  2. 2.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Barabási, A.L.: Network science: luck or reason. Nature 489(7417), 507 (2012)CrossRefGoogle Scholar
  4. 4.
    Li, Y., Meng, X.F., Zhang, Q., Zhang, J., Wang, C.Q.: Common patterns of online collective attention flow. Sci. China Inf. Sci. 60(5), 59102 (2017)CrossRefGoogle Scholar
  5. 5.
    Wu, F., Huberman, B.A.: Novelty and collective attention. Proc. Natl. Acad. Sci. 104(45), 17599–17601 (2007)CrossRefGoogle Scholar
  6. 6.
    Lou, X., Li, Y., Gu, W., Zhang, J.: The atlas of Chinese world wide web ecosystem shaped by the collective attention flows. PLoS ONE 11(11), e0165240 (2016)CrossRefGoogle Scholar
  7. 7.
    Shi, P., Huang, X., Wang, J., Zhang, J., Deng, S., Wu, Y.: A geometric representation of collective attention flows. PLoS ONE 10(9), e0136243 (2015)CrossRefGoogle Scholar
  8. 8.
    Aiello, W., Bonato, A., Cooper, C., Janssen, J., Prałat, P.: A spatial web graph model with local influence regions. Internet Math. 5(1–2), 175–196 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Wu, L., Zhang, J.: The decentralized flow structure of clickstreams on the web. Eur. Phys. J. B 86(6), 266 (2013)CrossRefGoogle Scholar
  10. 10.
    Li, Y., Zhang, J., Meng, X.F., Wang, C.Q.: Quantifying the influence of websites based on online collective attention flow. J. Comput. Sci. Technol. 30(6), 1175–1187 (2015)CrossRefGoogle Scholar
  11. 11.
    Sharma, R., Montesi, D.: Investigating similarity of nodes’ attributes in topological based communities. In: The Web Conference, pp. 1253–126 (2018)Google Scholar
  12. 12.
    Hutair, M.B., Aghbari, Z.A., Kamel, I.: Social community detection based on node distance and interest. In: 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 274–289. ACM (2016)Google Scholar
  13. 13.
    Forsati, R., Barjasteh, I., Ross, D., Esfahanian, A.H., Radha, H.: Network completion by leveraging similarity of nodes. Soc. Netw. Anal. Min. 6(1), 102 (2016)CrossRefGoogle Scholar
  14. 14.
    Masrour, F., Barjesteh, I., Forsati, R., Esfahanian, A.H., Radha, H.: Network completion with node similarity: a matrix completion approach with provable guarantees. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 302–307. ACM (2015)Google Scholar
  15. 15.
    Mollgaard, A., Zettler, I., Dammeyer, J., Jensen, M.H., Lehmann, S., Mathiesen, J.: Measure of node similarity in multilayer networks. PLoS ONE 11(6), e0157436 (2016)CrossRefGoogle Scholar
  16. 16.
    Conte, A., Ferraro, G., Grossi, R., Marino, A., Sadakane, K., Uno, T.: Node similarity with q-Grams for real-world labeled networks. In: 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1282–1291. ACM (2018)Google Scholar
  17. 17.
    Zhang, Q., Li, M., Deng, Y.: Measure the structure similarity of nodes in complex networks based on relative entropy. Phys. A: Stat. Mech. Appl. 491, 749–763 (2018)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Janssen, J., Prałat, P., Wilson, R.: Estimating node similarity from co-citation in a spatial graph model. In: ACM Symposium on Applied Computing, pp. 1329–1333. ACM (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Manfu Ma
    • 1
    • 2
  • Zhangyun Gong
    • 1
    • 2
  • Yong Li
    • 1
    • 2
    Email author
  • Huifang Li
    • 1
  • Qiang Zhang
    • 1
    • 2
  • Xiaokang Zhang
    • 3
  • Changqing Wang
    • 4
  1. 1.College of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina
  2. 2.Gansu IOT Research CenterLanzhouChina
  3. 3.Lanzhou Qidu Data Technology Co., Ltd.LanzhouChina
  4. 4.DNSLABChina Internet Network Information CenterBeijingChina

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