Advertisement

Rising Star Evaluation Based on Extreme Learning Machine in Geo-Social Networks

  • Yuliang MaEmail author
  • Ye Yuan
  • Guoren Wang
  • Xin Bi
  • Zhongqing Wang
  • Yishu Wang
Article
  • 42 Downloads

Abstract

In social networks, rising stars are junior individuals who may be not so charming at first but turn out to be outstanding over time. Recently, rising star evaluation has become a popular research topic in the field of social analysis, which is helpful for decision support, cognitive computation, and other practical problems. In this paper, we study the problem of rising star evaluation in geo-social networks. Specifically, given a topic keyword Q and a time point t, we aim at evaluating the latent influence of users to find rising stars, which refer to experts who have few activities and little impact currently on the underlying geo-social network but may become influential experts in the future. To efficiently evaluate future stars, we propose a novel processing framework based on extreme learning machine (ELM) called FS-ELM. FS-ELM consists of three key components. The first component constructs features by incorporating social topology and user behavior patterns. The second component extracts supervised information by discovering topic experts of Q at time (t + Δt); that is, excluding those detected at time t, topic experts obtained at time (t + Δt) can be regarded as rising stars at time t. The third component is ELM-based future star classification that leverages ELM as a departure point to evaluate whether a user is a rising star. Our experimental studies conducted on real-world datasets show that (1) FS-ELM can effectively discover rising stars with a query topic at time t and outperform other traditional methods and (2) user social characteristics have an important impact on the rising star evaluation. This paper studies a novel problem, namely, rising star evaluation in geo-social networks. We propose an advanced processing framework based on ELM by exploiting social topology characteristics and user behavior patterns. The experimental results encouragingly demonstrate the efficiency and effectiveness of the proposed approach.

Keywords

Extreme learning machine Geo-social network Rising star 

Notes

Funding Information

This research is partially funded by the National Key Research and Development Program of China (Grant No. 2016YFC1401900), the National Natural Science Foundation of China (Grant Nos. 61572119, 61572121, 61622202, 61732003, 61729201, 61702086, and U1401256), the Fundamental Research Funds for the Central Universities (Grant Nos. N171604007, and N171904- 007), the Natural Science Foundation of Liaoning Province (Grant no. 201705201- 64), and the China Postdoctoral Science Foundation (Grant no. 2018M631806).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained from all individual participants.

Human and Animal Rights

This article does not contain any studies involving human participants and/or animals by any of the authors.

References

  1. 1.
    Baeza-Yates RA, Ribeiro-Neto B. 2011. Modern information retrieval. China Machine Press.Google Scholar
  2. 2.
    Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Machx Learn Res Arch 2003;3:993–1022.Google Scholar
  3. 3.
    Daud A, Abbasi R, Muhammad F. Finding rising stars in social networks. International conference on database systems for advanced applications. Springer; 2013. p. 13–24.Google Scholar
  4. 4.
    Daud A, Aljohani NR, Abbasi RA, Rafique Z, Amjad T, Dawood H, Alyoubi KH. Finding rising stars in co-author networks via weighted mutual influence. In: Proceedings of the 26th international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee; 2017. p. 33–41.Google Scholar
  5. 5.
    Deng C, Wang S, Li Z, Huang GB, Lin W. 2017. Content-insensitive blind image blurriness assessment using weibull statistics and sparse extreme learning machine. IEEE Transactions on Systems, Man, and Cybernetics, Systems.Google Scholar
  6. 6.
    Ding F, Liu Y, Chen X, Chen F. 2018. Rising star evaluation in heterogeneous social network. IEEE Access.Google Scholar
  7. 7.
    Huang GB, Chen L, Siew CK, et al. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 2006;17(4):879–892.CrossRefGoogle Scholar
  8. 8.
    Huang GB, Siew CK. Extreme learning machine: RBF network case. In: CARCV 2004 8th control, automation, robotics and vision conference, 2004. IEEE; 2004. vol. 2, p. 1029–1036.Google Scholar
  9. 9.
    Huang GB, Wang DH, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cybern 2011;2 (2):107–122.CrossRefGoogle Scholar
  10. 10.
    Huang GB, Zhu QY, Siew CK. Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE international joint conference on neural networks, 2004. IEEE; 2004. vol. 2, p. 985–990.Google Scholar
  11. 11.
    Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing 2006; 70(1):489–501.CrossRefGoogle Scholar
  12. 12.
    Lahoti P, De Francisci Morales G, Gionis A. Finding topical experts in twitter via query-dependent personalized pagerank. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017. ACM; 2017. p. 155–162.Google Scholar
  13. 13.
    Lappas T, Liu K, Terzi E. Finding a team of experts in social networks. In: ACM SIGKDD International conference on knowledge discovery and data mining; 2009. p. 467–476.Google Scholar
  14. 14.
    Lauren P, Qu G, Yang J, Watta P, Huang GB, Lendasse A. Generating word embeddings from an extreme learning machine for sentiment analysis and sequence labeling tasks. Cogn Comput 2018;10(4):625–638.CrossRefGoogle Scholar
  15. 15.
    Li CT, Shan MK. Team formation for generalized tasks in expertise social networks. In: IEEE Second international conference on social computing; 2010. p. 9–16.Google Scholar
  16. 16.
    Li G, Chen S, Feng J, Li WS, Li WS. Efficient location-aware influence maximization. In: ACM SIGMOD International conference on management of data; 2014. p. 87–98.Google Scholar
  17. 17.
    Li N, Chen G. Multi-layered friendship modeling for location-based mobile social networks. In:2009 International mobile and ubiquitous systems: NETWORKING and services, mobiquitous. MOBIQUITOUS ’09; 2009. p. 1–10.Google Scholar
  18. 18.
    Li XL, Foo CS, Tew KL, Ng SK. Searching for rising stars in bibliography networks. In: International conference on database systems for advanced applications. Springer; 2009. p. 288–292.Google Scholar
  19. 19.
    Liang C, Liu Z, Sun M. 2012. Expert finding for microblog misinformation identification. In: COLING 2012: Posters; 2012. p. 703–712.Google Scholar
  20. 20.
    Liu H, Fang J, Xu X, Sun F. Surface material recognition using active multi-modal extreme learning machine. Cogn Comput 2018;10(6):937–950.CrossRefGoogle Scholar
  21. 21.
    Liu W, Sun W, Chen C, Huang Y, Jing Y, Chen K. Circle of friend query in Geo-Social networks. Berlin: Springer; 2012.CrossRefGoogle Scholar
  22. 22.
    Ma Y, Yuan Y, Wang G, Bi X, Qin H. Trust-aware personalized route query using extreme learning machine in location-based social networks. Cogn Comput 2018;10(6):965–979.CrossRefGoogle Scholar
  23. 23.
    Ma Y, Yuan Y, Wang G, Bi X, Wang Y. Personalized geo-social group queries in location-based social networks. In: International conference on database systems for advanced applications; 2018. p. 388–405.Google Scholar
  24. 24.
    Newman ME. Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Physical Review E 2001;64(1):016132.CrossRefGoogle Scholar
  25. 25.
    Ning Z, Liu Y, Kong X. Social gene—a new method to find rising stars. In: 2017 international symposium on networks, computers and communications (ISNCC). IEEE; 2017. p. 1–6.Google Scholar
  26. 26.
    Ning Z, Liu Y, Zhang J, Wang X. Rising star forecasting based on social network analysis. IEEE Access 2017;5:24229–24238.CrossRefGoogle Scholar
  27. 27.
    Page L. The pagerank citation ranking : Bringing order to the web. Stanford Digit Libr Work Paper 1999;9(1): 1–14.Google Scholar
  28. 28.
    Wang S, Deng C, Lin W, Huang GB, Zhao B. Nmf-based image quality assessment using extreme learning machine. IEEE Trans Cybern 2017;47(1):232–243.CrossRefGoogle Scholar
  29. 29.
    Wei W, Cong G, Miao C, Zhu F, Li G. Learning to find topic experts in twitter via different relations. IEEE Trans Knowl Data Eng 2016;28(7):1764–1778.CrossRefGoogle Scholar
  30. 30.
    Weng J, Lim EP, Jiang J, He Q. Twitterrank: finding topic-sensitive influential twitterers. In: ACM International conference on web search and data mining; 2010. p. 261–270.Google Scholar
  31. 31.
    Yang D. N, Shen C. Y, Lee W. C, Chen M. S. On socio-spatial group query for location-based social networks. In: ACM SIGKDD International conference on knowledge discovery and data mining; 2012. p. 949–957.Google Scholar
  32. 32.
    Yuan Y, Lian X, Chen L, Sun Y, Wang G. Rsknn: knn search on road networks by incorporating social influence. IEEE Trans Knowl Data Eng 2016;28(6):1575–1588.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNortheastern UniversityLiaoningChina
  2. 2.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  3. 3.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityLiaoningChina
  4. 4.Information CenterThe First Hospital of China Medical UniversityShenyangChina

Personalised recommendations