Abstract
Social coding is a software development approach, which can facilitate hundreds of developers collaborating in one project simultaneously. Many researchers focus on the analysis of social network based on complex network models. However, the traditional complex network model cannot express the full information of collaboration. In this paper, in order to depict the properties of hypernetwork well and get a good simulation result, we investigate the time evolution of the GitHub dataset. We find that, (1) The hypernetworks show high level of self-organization; (2) From the neighbor connectivity of developers, some of the skilled developers wish to collaborate with skilled developers, whereas some skilled developers prefer to collaborate with freshman; (3) From the statistical properties of programming languages communities, the assortativity of Java community is obviously different from other communities, and the projects have a high probability of collaboration with those using the same programming languages.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Berge, C., Minieka, E.: Graphs and Hypergraphs, vol. 7. North-Holland Publishing Company, Amsterdam (1973)
Dabbish, L., Stuart, C., Tsay, J., Herbsleb, J.: Social coding in GitHub: transparency and collaboration in an open software repository. In: Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, pp. 1277–1286. ACM (2012)
Davis, G.F., Greve, H.R.: Corporate elite networks and governance changes in the 1980s. Am. J. Social. 103(1), 1–37 (1997)
Douglas, P.H.: The Cobb-Douglas production function once again: its history, its testing, and some new empirical values. J. Polit. Econ. 84(5), 903–915 (1976)
Gallagher, S.R., Goldberg, D.S.: Clustering coefficients in protein interaction hypernetworks. In: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, p. 552. ACM (2013)
Ghoshal, G., Zlatić, V., Caldarelli, G., Newman, M.: Random hypergraphs and their applications. Phys. Rev. E 79(6), 066118 (2009)
Gousios, G.: The GHTorrent dataset and tool suite. In: Proceedings of the 10th Working Conference on Mining Software Repositories, MSR 2013, pp. 233–236. IEEE Press, Piscataway (2013)
Gousios, G., Spinellis, D.: GHTorrent: GitHub’s data from a firehose. In: 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), pp. 12–21. IEEE (2012)
Guang-Yong, Y., Jian-Guo, L.: A local-world evolving hypernetwork model. Chin. Phys. B 23(1), 018901 (2014)
Klamt, S., Haus, U.-U., Theis, F.: Hypergraphs and cellular networks. PLoS Comput. Biol. 5(5), e1000385 (2009)
Lambiotte, R., Ausloos, M.: Collaborative tagging as a tripartite network. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 1114–1117. Springer, Heidelberg (2006). doi:10.1007/11758532_152
Liu, J.-G., Yang, G.-Y., Hu, Z.-L.: A knowledge generation model via the hypernetwork. PloS One 9(3), e89746 (2014)
Newman, M.E.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. 98(2), 404–409 (2001)
Newman, M.E.: Assortative mixing in networks. Phys. Rev. Lett. 89(20), 208701 (2002)
Onoue, S., Hata, H., Matsumoto, K.-I.: A study of the characteristics of developers’ activities in GitHub. In: Software Engineering Conference (APSEC, 2013 20th Asia-Pacific), pp. 7–12. IEEE (2013)
Palla, G., Farkas, I.J., Pollner, P., Derényi, I., Vicsek, T.: Fundamental statistical features and self-similar properties of tagged networks. New J. Phys. 10(12), 123026 (2008)
Pastor-Satorras, R., Vázquez, A., Vespignani, A.: Dynamical and correlation properties of the internet. Phys. Rev. Lett. 87(25), 258701 (2001)
Ramasco, J.J., Dorogovtsev, S.N., Pastor-Satorras, R.: Self-organization of collaboration networks. Phys. Rev. E 70(3), 036106 (2004)
Singh, P.V., Tan, Y., Mookerjee, V.S.: Network effects: the influence of structural capital on open source project success. MIS Q. 35(4), 813–829 (2011)
Thung, F., Bissyandé, T.F., Lo, D., Jiang, L.: Network structure of social coding in GitHub. In: 2013 17th European Conference on Software Maintenance and Reengineering (CSMR), pp. 323–326. IEEE (2013)
Wang, J.-W., Rong, L.-L., Deng, Q.-H., Zhang, J.-Y.: Evolving hypernetwork model. Eur. Phys. J. B-Condens. Matter Complex Syst. 77(4), 493–498 (2010)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Zlatić, V., Ghoshal, G., Caldarelli, G.: Hypergraph topological quantities for tagged social networks. Phys. Rev. E 80(3), 036118 (2009)
Acknowledgment
This work is supported by National Nature Science Foundation of China [Grant Nos. 61773296, 61672391].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kuang, L., Wang, F., Zhang, H., Li, Y. (2017). Statistical Analysis of Social Coding in GitHub Hypernetwork. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_72
Download citation
DOI: https://doi.org/10.1007/978-3-319-68759-9_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68758-2
Online ISBN: 978-3-319-68759-9
eBook Packages: Computer ScienceComputer Science (R0)