Advertisement

Incorporating geographical location for team formation in social coding sites

  • Liang Chen
  • Yongjian Ye
  • Angyu Zheng
  • Fenfang Xie
  • Zibin ZhengEmail author
  • Michael R. Lyu
Article
  • 1 Downloads

Abstract

With the proliferation of open source software and community, more and more developers from different background (e.g., culture, language, location, skill) prefer to work collaboratively and release their works in social coding sites (e.g., Github). Given a collaborative project with a set of required skills, it is an important and challenging task to form a team of developers that have not only the required skills but also the minimal communication cost. Previous works mainly leverage historical collaboration records among team members to model the communication cost, while ignoring the impact of geographical location of each developer. In this paper, we aim to exploit and incorporate the geographical information to improve the performance of team formation in social coding sites. Specifically, we conduct two objective functions for the collaboration records and geographical proximity correspondingly, and propose two optimization algorithms. Comprehensive experiments on a real-world dataset (e.g., GitHub) demonstrate the performance of the proposed model with the comparison of some state-of-the-art ones.

Keywords

Team formation Geographical location Social coding sites Genetic algorithm 

Notes

Acknowledgements

The work described in this paper was supported by the National Key Research and Development Program (2017YFB0202200), the National Natural Science Foundation of China (61702568, U1711267), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (No.2017ZT07X355) and the Fundamental Research Funds for the Central Universities under Grant (17lgpy117).

References

  1. 1.
    Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp 839–848. ACM (2012)Google Scholar
  2. 2.
    Ashenagar, B., Eghlidi, N., Afshar, A., Hamzeh, A.: Team formation in social networks based on local distance metric. In: International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 946–952 (2015)Google Scholar
  3. 3.
    Balog, K., De Rijke, M.: Determining expert profiles (with an application to expert finding). IJCAI 7, 2657–2662 (2007)Google Scholar
  4. 4.
    Basiri, J., Taghiyareh, F., Ghorbani, A.: Collaborative team formation using brain drain optimization: A practical and effective solution. World Wide Web 20(6), 1385–1407 (2017)CrossRefGoogle Scholar
  5. 5.
    Baykasoglu, A., Dereli, T., Das, S.: Project team selection using fuzzy optimization approach. Cybern. Syst.: Int. J. 38(2), 155–185 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International Conference on Parallel Problem Solving from Nature, pp 849–858. Springer, Berlin (2000)Google Scholar
  7. 7.
    Farhadi, F., Sorkhi, M., Hashemi, S., Hamzeh, A.: An effective expert team formation in social networks based on skill grading. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp 366–372. IEEE (2011)Google Scholar
  8. 8.
    Fitzpatrick, E.L., Askin, R.G.: Forming effective worker teams with multi-functional skill requirements. Comput. Indust. Eng. 48(3), 593–608 (2005)CrossRefGoogle Scholar
  9. 9.
    Han, Y., Wan, Y., Chen, L., Xu, G., Wu, J.: Exploiting geographical location for team formation in social coding sites. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 499–510. Springer, Cham (2017)Google Scholar
  10. 10.
    Kargar, M., An, A.: Discovering top-k teams of experts with/without a leader in social networks. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2011, pp 985–994 (2011)Google Scholar
  11. 11.
    Kargar, M., An, A., Zihayat, M.: Efficient bi-objective team formation in social networks. Mach. Learn. Knowl. Discov. Databases, 483–498 (2012)Google Scholar
  12. 12.
    Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: A tutorial. Reliab. Eng. Syst. Safety 91(9), 992–1007 (2006)CrossRefGoogle Scholar
  13. 13.
    Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 467–476. ACM (2009)Google Scholar
  14. 14.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp 641–650. ACM (2010)Google Scholar
  15. 15.
    Li, C.T., Shan, M.K., Lin, S.D.: On team formation with expertise query in collaborative social networks. Knowl. Inf. Syst. 42(2), 441–463 (2015)CrossRefGoogle Scholar
  16. 16.
    Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y.R., Buchler, N.: Replacing the irreplaceable: Fast algorithms for team member recommendation. In: Proceedings of the 24th International Conference on World Wide Web, pp 636–646. International World Wide Web Conferences Steering Committee (2015)Google Scholar
  17. 17.
    Li, C.T., Huang, M.Y., Yan, R.: Team formation with influence maximization for influential event organization on social networks. World Wide Web, 1–21 (2017)Google Scholar
  18. 18.
    Majumder, A., Datta, S., Naidu, K.: Capacitated team formation problem on social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1005–1013. ACM (2012)Google Scholar
  19. 19.
    Ponds, R., Van Oort, F., Frenken, K.: The geographical and institutional proximity of research collaboration. Papers Reg. Sci. 86(3), 423–443 (2007)CrossRefGoogle Scholar
  20. 20.
    Rangapuram, S.S., Buhler, T., Hein, M.: Towards realistic team formation in social networks based on densest subgraphs. In: Proceedings of the 22nd International Conference on World Wide Web, pp 1077–1088. ACM (2013)Google Scholar
  21. 21.
    Wang, X., Zhao, Z., Ng, W.: A comparative study of team formation in social networks. In: 2015 International Conference on Database Systems for Advanced Applications (DASFAA), pp. 389–404 (2015)Google Scholar
  22. 22.
    Wang, X., Zhao, Z., Ng, W.: USTF: A unified system of team formation. IEEE Trans. Big Data 2(1), 70–84 (2016)CrossRefGoogle Scholar
  23. 23.
    Wi, H., Oh, S., Mun, J., Jung, M.: A team formation model based on knowledge and collaboration. Expert Syst. Appl. 36(5), 9121–9134 (2009)CrossRefGoogle Scholar
  24. 24.
    Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp 981–990. ACM (2010)Google Scholar
  25. 25.
    Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp 15–24. ACM (2016)Google Scholar
  26. 26.
    Yang, Y., Hu, H.: Team formation with time limit in social networks. In: International Conference on Mechatronic Sciences (MEC), pp. 1590–1594 (2013)Google Scholar
  27. 27.
    Yin, H., Cui, B.: Spatio-Temporal Recommendation in Social Media. Springer, Singapore (2016)CrossRefGoogle Scholar
  28. 28.
    Yin, H., Cui, B., Zhou, X., Wang, W., Huang, Z., Sadiq, S.: Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Trans. Inf. Syst. (TOIS) 35(2), 11 (2016)CrossRefGoogle Scholar
  29. 29.
    Yin, H., Hu, Z., Zhou, X., Wang, H., Zheng, K., Nguyen, Q.V.H., Sadiq, S.: Discovering interpretable geo-social communities for user behavior prediction. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp 942–953. IEEE (2016)Google Scholar
  30. 30.
    Yin, H., Chen, H., Sun, X., Wang, H., Wang, Y., Nguyen, Q.V.H.: SPTF: A scalable probabilistic tensor factorization model for semantic-aware behavior prediction. In: 2017 IEEE International Conference on Data Mining (ICDM), pp 585–594. IEEE (2017)Google Scholar
  31. 31.
    Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Trans. Knowl. Data Eng. 29(11), 2537–2551 (2017)CrossRefGoogle Scholar
  32. 32.
    Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for poi recommendation. IEEE Trans. Knowl. Data Eng. 28(10), 2566–2581 (2016)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Liang Chen
    • 1
  • Yongjian Ye
    • 1
  • Angyu Zheng
    • 1
  • Fenfang Xie
    • 1
  • Zibin Zheng
    • 1
    Email author
  • Michael R. Lyu
    • 2
  1. 1.School of Data and Computer ScienceSun Yat-Sen UniversityGuangzhouChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong

Personalised recommendations