Abstract
Open-source software (OSS) is widely used and has become an essential infrastructure for our society today. Substantial research has been done to improve the success of OSS development. Of them, studies about influencers have gained attention in recent times. Influencers are regarded as an evangelist in a specific domain, and play an important role in persuading others. They are frequently analyzed on Twitter and other SNSs. With the advent of social coding platforms such as GitHub, research has started on OSS influencers who seem to affect the behavior of developers. However, there is not yet enough research on the method of identifying influencers and their effects on OSS. In this study, we analyzed the follow-network of cryptocurrency projects developed on GitHub quantitatively, and found (1) The HITS algorithm is more effective when compared with in-degree centrality and PageRank algorithm in identifying influencers of a specific domain. (2) The rate of contribution of a user correlates with their rate of influence, but the explanatory power is small. The amount of activity on GitHub is not as essential for OSS influencers as it is on Twitter, which requires a lot of activity to be an influencer. (3) The rate of influence of influencers on a project correlates with the number of contributors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
http://coinmarketcap.com (accessed 2018-02-12).
- 2.
https://www.coingecko.com (accessed 2018-02-12).
- 3.
http://infolab.stanford.edu/~backrub/google.html (accessed 2019-01-20).
References
Badashian, A.S., Stroulia, E.: Measuring user influence in GitHub. In: Proceedings of the 3rd International Workshop on CrowdSourcing in Software Engineering - CSI-SE 2016, pp. 15–21 (2016). https://doi.org/10.1145/2897659.2897663
Blincoe, K., Sheoran, J., Goggins, S., Petakovic, E., Damian, D.: Understanding the popular users: following, affiliation influence and leadership on GitHub. Inf. Softw. Technol. 70, 30–39 (2016). https://doi.org/10.5935/0004-2749.20180056
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K., et al.: Measuring user influence in Twitter: the million follower fallacy. ICWSM 10(10–17), 30 (2010)
Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. SIAM Rev. 51(4), 661–703 (2009). https://doi.org/10.1137/070710111
Cosentino, V., Luis, J., Cabot, J.: Findings from GitHuB: methods, datasets and limitations. In: Proceedings of the 13th International Conference on Mining Software Repositories, pp. 137–141. ACM (2016)
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)
GitHub: Celebrating nine years of GitHuB with an anniversary sale. https://github.com/blog/2345-celebrating-nine-years-of-github-with-an-anniversary-sale
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. AcM (2010)
Lima, A., Rossi, L., Musolesi, M.: Coding together at scale: GitHub as a collaborative social network, pp. 295–304 (2014). https://doi.org/10.13140/2.1.4625.4880
Newman, M.E.: Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)
Thung, F., Bissyandé, T.F., Lo, D., Jiang, L.: Network structure of social coding in GitHub. In: Proceedings of the European Conference on Software Maintenance and Reengineering, CSMR, pp. 323–326 (2013). https://doi.org/10.1109/CSMR.2013.41
Tsay, J., Dabbish, L., Herbsleb, J.D.: Social media in transparent work environments. In: 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering, CHASE 2013 - Proceedings, pp. 65–72 (2013). https://doi.org/10.1109/CHASE.2013.6614733
Yu, Y., Yin, G., Wang, H., Wang, T.: Exploring the patterns of social behavior in GitHub. In: Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies, pp. 31–36 (2014). https://doi.org/10.1145/2666539.2666571
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kobayakawa, N., Yoshida, K. (2019). Study on Influencers of Cryptocurrency Follow-Network on GitHub. In: Ohara, K., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2019. Lecture Notes in Computer Science(), vol 11669. Springer, Cham. https://doi.org/10.1007/978-3-030-30639-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-30639-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30638-0
Online ISBN: 978-3-030-30639-7
eBook Packages: Computer ScienceComputer Science (R0)