Advertisement

World Wide Web

, Volume 22, Issue 2, pp 533–553 | Cite as

ProfitLeader: identifying leaders in networks with profit capacity

  • Zhongjing Yu
  • Junming ShaoEmail author
  • Qinli Yang
  • Zejun Sun
Article
  • 164 Downloads
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

Abstract

Identifying ‘Leaders’ in a network is a significant step to optimize the use of available resources, accelerate or hinder spreading information. In this paper, we propose a new measure to characterize the importance of a node, called ProfitLeader, which considers the problem of critical node identification from a novel perspective: profit capacity. Unlike established approaches to identify influential nodes in networks based on its local topology or information diffusion capacity, ProfitLeader, ranks the influence of each individual node via its profit capacity. The basic idea is to view each node as a person, and measure how much profit “the person” can offer for others. Namely, the more profit “the person” provides for others, the more important the node is. To characterize the profit capacity of each node, its available resource and sharing probability to its neighbors are investigated. Building upon the profit capacity, ProfitLeader has several remarkable advantages: (a) It provides a simple, intuitive yet effective way to rank the nodes in networks from a new viewpoint; (b) ProfitLeader allows finding the most important nodes (with high quality). (c) ProfitLeader is fully automatic, and no parameters are required to be specified by users. Extensive experiments on both small networks and large-scale networks have demonstrated its superiority over several state-of-the-art algorithms.

Keywords

Network mining Critical node identification Profit capacity 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61403062, 61433014, 41601025), China Postdoctoral Science Foundation (2014M552344,2015M580786), Science-Technology Foundation for Young Scientist of Sichuan Province (2016JQ0007), National key research and development program (2016YFB0502300),and Sichuan province soft science research project (2017ZR0208).

References

  1. 1.
    Aristotle, J.: Betweenness centrality. ACM Sigplan Not. 48(8) (2013)Google Scholar
  2. 2.
    Bao, Z., Ma, C., Xiang, B., Zhang, H.: Identification of influential nodes in complex networks: Method from spreading probability viewpoint. Physica A: Statist. Mech. Appl. 468, 391–397 (2017)CrossRefzbMATHGoogle Scholar
  3. 3.
    Boguñá, M., Castellano, C., Pastor-Satorras, R.: Nature of the epidemic threshold for the susceptible-infected-susceptible dynamics in networks. Phys. Rev. Lett. 111(6), 068,701 (2013)CrossRefGoogle Scholar
  4. 4.
    Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2(1), 113–120 (1972)CrossRefGoogle Scholar
  5. 5.
    Bonacich, P., Lloyd, P.: Eigenvector-like measures of centrality for asymmetric relations. Soc. Netw. 23(3), 191–201 (2001)CrossRefGoogle Scholar
  6. 6.
    Bornmann, L., Daniel, H.D.: The state of h index research. EMBO Rep. 10 (1), 2–6 (2009)CrossRefGoogle Scholar
  7. 7.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. World Wide Web 30, 107–117 (1998)Google Scholar
  8. 8.
    Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: IEEE International Conference on Data Mining, pp. 88–97 (2010)Google Scholar
  9. 9.
    Chen, D., Lü, L., Shang, M., Zhang, Y., Zhou, T.: Identifying influential nodes in complex networks. Physica A: Statist. Mech. Appl. 391(4), 1777–1787 (2012)CrossRefGoogle Scholar
  10. 10.
    Cohen, R., Erez, K., Benavraham, D., Havlin, S.: Resilience of the internet to random breakdowns. Phys. Rev. Lett. 85(21), 4626–4628 (2000)CrossRefGoogle Scholar
  11. 11.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)CrossRefGoogle Scholar
  12. 12.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  13. 13.
    Gao, L., Song, J., Nie, F., Zou, F., Sebe, N., Shen, H.T.: Graph-without-cut: An ideal graph learning for image segmentation. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1188–1194 (2016)Google Scholar
  14. 14.
    Gao, L., Song, J., Liu, X., Shao, J., Liu, J., Shao, J.: Learning in high-dimensional multimedia data: The state of the art. Multimed. Syst. 23(3), 303–313 (2017)CrossRefGoogle Scholar
  15. 15.
    Garas, A., Schweitzer, F., Havlin, S.: A k-shell decomposition method for weighted networks. J. Phys. 14(8), 083,030 (2012)Google Scholar
  16. 16.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefzbMATHGoogle Scholar
  17. 17.
    Kempe, D., Kleinberg, J., Tardos, É. : Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
  18. 18.
    Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1–2), 81–93 (1938)CrossRefzbMATHGoogle Scholar
  19. 19.
    Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)CrossRefGoogle Scholar
  20. 20.
    Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87(19), 198,701 (2001)CrossRefGoogle Scholar
  21. 21.
    Li, X., Cai, H., Huang, Z., Yang, Y., Zhou, X.: Social event identification and ranking on flickr. World Wide Web 18(5), 1219–1245 (2015)CrossRefGoogle Scholar
  22. 22.
    Li, Y., Zhang, D., Tan, K.L.: Real-time targeted influence maximization for online advertisements. Proc. VlDB Endow. 8(10), 1070–1081 (2015)CrossRefGoogle Scholar
  23. 23.
    Li, X., Liu, Y., Jiang, Y., Liu, X.: Identifying social influence in complex networks: A novel conductance eigenvector centrality model. Neurocomputing 210, 141–154 (2016)CrossRefGoogle Scholar
  24. 24.
    Li, Y., Shang, Y., Yang, Y.: Clustering coefficients of large networks. Inform. Sci. 382, 350–358 (2017)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Liu, Y., Tang, M., Zhou, T., Do, Y.: Improving the accuracy of the k-shell method by removing redundant links: From a perspective of spreading dynamics. Scientif. Rep. 5(1), 13,172–13,172 (2015)CrossRefGoogle Scholar
  26. 26.
    Liu, Z., Jiang, C., Wang, J., Yu, H.: The node importance in actual complex networks based on a multi-attribute ranking method. Knowl.-Based Syst. 84, 56–66 (2015)CrossRefGoogle Scholar
  27. 27.
    Liu, Y., Tang, M., Zhou, T., Do, Y.: Identify influential spreaders in complex networks, the role of neighborhood. Physica A: Statist. Mech. Appl. 452, 289–298 (2016)CrossRefGoogle Scholar
  28. 28.
    Liu, Y., Tang, M., Do, Y., Hui, P.: Accurate ranking of influential spreaders in networks based on dynamically asymmetric link weights. Phys. Rev. E 96 (2), 022,323 (2017)CrossRefGoogle Scholar
  29. 29.
    Lü, L., Chen, D., Ren, X., Zhang, Q., Zhang, Y., Zhou, T.: Vital nodes identification in complex networks. Phys. Rep. 650, 1–63 (2016)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Lü, L., Zhou, T., Zhang, Q., Stanley, H.E.: The h-index of a network node and its relation to degree and coreness. Nat.Commun. 7, 10,168–10,168 (2016)CrossRefGoogle Scholar
  31. 31.
    Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)CrossRefGoogle Scholar
  32. 32.
    Newman, M.E.J.: Spread of epidemic disease on networks. Quantit. Biol. 66 (1–2), 016,128 (2002)MathSciNetGoogle Scholar
  33. 33.
    Rahimkhani, K., Aleahmad, A., Rahgozar, M., Moeini, A.: A fast algorithm for finding most influential people based on the linear threshold model. Expert Syst. Appl. 42(3), 1353–1361 (2015)CrossRefGoogle Scholar
  34. 34.
    Rizzi, A., Iacovazzi, A., Baiocchi, A., Colabrese, S.: A low complexity real-time internet traffic flows neuro-fuzzy classifier. Comput. Netw. 91, 752–771 (2015)CrossRefGoogle Scholar
  35. 35.
    Shao, J., Böhm, C., Yang, Q., Plant, C.: Synchronization Based Outlier Detection. Springer, Berlin (2010)CrossRefGoogle Scholar
  36. 36.
    Shao, J., Han, Z., Yang, Q., Zhou, T.: Community detection based on distance dynamics. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1075–1084 (2015)Google Scholar
  37. 37.
    Shao, J., Yang, Q., Dang, H., Schmidt, B., Kramer, S.: Scalable clustering by iterative partitioning and point attractor representation. ACM Trans. Knowl. Discov. Data 11(1), 5 (2016)CrossRefGoogle Scholar
  38. 38.
    Shao, J., Wang, X., Yang, Q., Plant, C., Bohm, C.: Synchronization-based scalable subspace clustering of high-dimensional data. Knowl. Inf. Syst. 52(1), 83–111 (2017)CrossRefGoogle Scholar
  39. 39.
    Song, J., Yang, Y., Li, X., Huang, Z., Yang, Y.: Robust hashing with local models for approximate similarity search. IEEE Trans. Syst. Man Cybern. 44(7), 1225–1236 (2014)Google Scholar
  40. 40.
    Song, J., Gao, L., Nie, F., Shen, H., Yan, Y., Sebe, N.: Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans. Image Process. 25(11), 4999–5011 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Song, J., Gao, L., Puscas, M.M., Nie, F., Shen, F., Sebe, N.: Joint graph learning and video segmentation via multiple cues and topology calibration. In: ACM on Multimedia Conference, pp. 831–840 (2016)Google Scholar
  42. 42.
    Song, J., Gao, L., Zou, F., Yan, Y., Sebe, N.: Deep and fast: Deep learning hashing with semi-supervised graph construction. Image Vis. Comput. 55, 101–108 (2016)CrossRefGoogle Scholar
  43. 43.
    Sun, Z., Wang, B., Sheng, J., Hu, Y., Wang, Y., Shao, J.: Identifying influential nodes in complex networks based on weighted formal concept analysis. IEEE Access 5(99), 3777–3789 (2017)CrossRefGoogle Scholar
  44. 44.
    Wang, S., Du, Y., Deng, Y.: A new measure of identifying influential nodes: Efficiency centrality. Commun. Nonlinear Sci. Numer. Simul. 47, 151–163 (2017)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Wang, Z., Du, C., Fan, J., Xing, Y.: Ranking influential nodes in social networks based on node position and neighborhood. Neurocomputing (2017)Google Scholar
  46. 46.
    Xiong, X., Ma, J., Wang, M., Zhou, G., Xu, K.: Information diffusion model in modular microblogging networks. World Wide Web 18(4), 1051–1069 (2015)CrossRefGoogle Scholar
  47. 47.
    Xu, S., Wang, P.: Identifying important nodes by adaptive leaderrank. Physica A: Statist. Mech. Appl. 469, 654–664 (2017)CrossRefGoogle Scholar
  48. 48.
    Zhao, J., Wu, J., Chen, M., Fang, Z., Zhang, X., Xu, K.: K-core-based attack to the internet: Is it more malicious than degree-based attack? World Wide Web 18(3), 749–766 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Zhongjing Yu
    • 1
  • Junming Shao
    • 1
    Email author
  • Qinli Yang
    • 1
  • Zejun Sun
    • 2
  1. 1.Big Data Research Center, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Information Science and EngineeringCentral South UniversityChangshaChina

Personalised recommendations