single-jc.php

JACIII Vol.21 No.4 pp. 650-658
doi: 10.20965/jaciii.2017.p0650
(2017)

Paper:

Prediction of Rising Venues in Citation Networks

Muhammad Azam Zia, Zhongbao Zhang, Guangda Li, Haseeb Ahmad, and Sen Su

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
10 Xitucheng Rd., Haidian District, Beijing 100876, China

Received:
March 6, 2017
Accepted:
May 19, 2017
Published:
July 20, 2017
Keywords:
prediction, rising star, social network, venues
Abstract

Prediction of rising stars has become a core issue in data mining and social networks. Prediction of rising venues could unveil rapidly emerging research venues in citation network. The aim of this research is to predict the rising venues. First, we presented five effective prediction features along with their mathematical formulations for extracting rising venues. The underlying features are composed by incorporating the citation count, publications, cited to and cited by information at venue level. For prediction purpose, we employ four machine learning algorithms including Bayesian Network, Support Vector Machine, Multilayer Perceptron and Random Forest. Experimental results demonstrate that proposed features set are effective for rising venues prediction. Our empirical analysis spotlights the rising venues that demonstrate the continuous improvement over time and finally become the leading scientific venues.

References
  1. [1] Z.-S. Wang, J.-F. Juang, and W.-G. Teng, “Predicting POI Visits in a Heterogeneous Location-Based Social Network,” J. of Adv. Comput. Intell. Intell. Inform., Vol.20, No.6, pp. 882-892, 2016.
  2. [2] S. Usui, F. Toriumi, M. Matsuo, T. Hirayama, and K. Mase, “Greedy Network Growth Model of Social Network Service,” J. of Adv. Comput. Intell. Intell. Inform., Vol.18, No. 4, pp. 590-597, 2014.
  3. [3] http://www.Twitter.com [accessed January 1, 2017]
  4. [4] http://www.weibo.com [accessed January 15, 2017]
  5. [5] http://dblp.uni-trier.de [accessed August 12, 2016]
  6. [6] Mining deep knowledge from scientific networks, https://cn.aminer.org/ [accessed January 15, 2017]
  7. [7] J. Zhang, F. Xia, W. Wang, X. Bai, S. Yu, T. M. Bekele, and Z. Peng, “Cocarank: A collaboration caliber-based method for finding academic rising stars,” Proc. of the 25th Int. Conf. Companion on World Wide Web, pp. 395-400, 2016.
  8. [8] G. Tsatsaronis, I. Varlamis, S. Torge, M. Reimann, K. Nørvåg, M. Schroeder, and M. Zschunke, “How to become a group leader? or modeling author types based on graph mining,” Int. Conf. on Theory and Practice of Digital Libraries, Springer Berlin Heidelberg, pp. 15-26, 2011.
  9. [9] A. Ibáñez, P. Larrañaga, and C. Bielza, “Predicting citation count of Bioinformatics papers within four years of publication,” Bioinformatics, Vol.25, No.24, pp. 3303-3309, 2009.
  10. [10] R. Yan, J. Tang, X. Liu, D. Shan, and X. Li, “Citation count prediction: learning to estimate future citations for literature,” Proc. of the 20th ACM Int. Conf. on Information and knowledge management, pp. 1247-1252, 2011.
  11. [11] R. Yan, C. Huang, J. Tang, Y. Zhang, and X. Li, “To better stand on the shoulder of giants,” Proc. of the 12th ACM/IEEE-CS Joint Conf. on Digital Libraries, Washington, pp. 51-60, 2012.
  12. [12] H. S. Bhat, L.-H. Huang, S. Rodriguez, R. Dale, and E. Heit, “Citation prediction using diverse features,” ICDMW, IEEE Int. Conf., pp. 589-596, 2015.
  13. [13] M. Nezhadbiglari, M. A. Gonçalves, and J. M. Almeida, “Early prediction of scholar popularity,” Proc. of the 16th ACM/IEEE-CS on Joint Conf. on Digital Libraries, New Jersey, USA, pp. 181-190, 2016.
  14. [14] Y. Dong, R. A. Johnson, Y. Yang, and N. V. Chawla,“Collaboration signatures reveal scientific impact,” Advances in Social Networks Analysis and Mining (ASONAM), pp. 480-487, 2015.
  15. [15] Y. Dong, R. A. Johnson, and N. V. Chawla, “Will this paper increase your h-index?: Scientific impact prediction,” Proc. of the 8th ACM Int. Conf. on Web Search and Data Mining, Shanghai, China, pp. 149-158, 2015.
  16. [16] X.-L. Li, C. S. Foo, K. L. Tew, and S.-K. Ng, “Searching for rising stars in bibliography networks,” Int. Conf. on Database Systems for Advanced Applications, Springer Berlin Heidelberg. pp. 288-292, 2009.
  17. [17] A. Daud, R. Abbasi, and F. Muhammad, “Finding rising stars in social networks,” Int. Conf. on Database Systems for Advanced Applications, Springer Berlin Heidelberg, pp. 13-24, 2013.
  18. [18] A. Daud, M. Ahmad, M. S. I. Malik, and D. Che, “Using machine learning techniques for rising star prediction in co-author network,” Scientometrics, Vol.102, No.2, pp. 1687-1711, 2015.
  19. [19] S. M. Billah and S. Gauch, “Social network analysis for predicting emerging researchers,” Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), pp. 27-35, 2015.
  20. [20] H. Ahmad, A. Daud, L. Wang, H. Hong, H. Dawood, and Y. Yang, “Prediction of Rising Stars in the Game of Cricket,” IEEE Access, Vol.5, No.1, pp. 4104-4124, 2017.
  21. [21] M. A. Zia, Z. Zhang, L. Ximing, H. Ahmad, and S. Su, “ComRank: Joint Weight Technique for the Identification of Influential Communities,” China Communications, Vol.14, No.4, pp. 101-110, 2017.
  22. [22] P. L. López-Cruz, P. Larrañaga, J. DeFelipe, and C. Bielza, “Bayesian network modeling of the consensus between experts: An application to neuron classification,” Int. J. of Approximate Reasoning, Vol.55, No.1, pp. 3-22, 2014.
  23. [23] I. Steinwart and C. Andreas, “Support Vector Machines,” Springer-Verlag, 2008.
  24. [24] R. Collobert and S. Bengio, “Links between Perceptrons, MLPs and SVMs,” Proc. Int. Conf. on Machine Learning (ICML), Alberta Canada, 2004.
  25. [25] L. Breiman, “Random forests,” Machine Learning, Vol.45, No.1, pp. 5-32, 2001.
  26. [26] J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su, “Arnetminer: extraction and mining of academic social networks,” Proc. of the 14th ACM SIGKDD Int. Conf. on Knowledge discovery and data mining, Nevada, USA, pp. 990-998, 2008.
  27. [27] M. A. Hasan, V. Chaoji, S. Salem, and M. Zaki, “Link prediction using supervised learning,” SDM06: Workshop on link analysis, counter-terrorism and security, 2006.
  28. [28] https://cn.aminer.org/ranks/conf [accessed January 20, 2017]

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Aug. 21, 2017