Study on Influencers of Cryptocurrency Follow-Network on GitHub

Kobayakawa, Naoki; Yoshida, Kenichi

doi:10.1007/978-3-030-30639-7_15

Cited by 2 publications

(2 citation statements)

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“…Node centrality measures can be applied to understand the influential behaviour of users. For example, HITS [13] and PageRank [10] are usually the standard to identify influential nodes in a network. In one of such studies, a custom metric called "DevRank" has been devised to mine the influential users based upon their followers count and code commit [12].…”

Section: Related Workmentioning

confidence: 99%

“…Existing studies have explained the user influence and its dynamics on GitHub to some extent, however some of the research gaps still exist [10]. To systematically tackle these research questions, we present an exploratory analysis to compare different metrics including a) the number of followers [11,12,13], b) HITS [13] and PageRank [10] values, and c) user activity metrics such as pull-requests and issues [14]. We also conduct observations at the macroscopic level, such as the in-degree distribution and the growth pattern of the follower network over the years, and compare the result with other networks such as academic citation networks as well as analytical models, where the first mover advantage may or may not exist.…”

Section: Introductionmentioning

confidence: 99%

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Does the First Mover Advantage Exist on GitHub?

Mehta,

Paudyal,

Sharma

et al. 2020

Preprint

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Collaborative consensus-finding is an integral element of many Web services and greatly determines the quality of information, content, and products that are available through the Web. That also means that the dynamics of democratic consensus-finding strengthen collective resilience against potential threats that attempt to degrade information, content, and products and affect Web data, users, behaviors, and even beyond as well as offline life. Even on Web platforms that are open to all, the influence of some first mover authors may shape future discussion and collaboration, which is comparable to academic citation networks for instance. In a social coding network such as GitHub, activities of a set of users can have influence on other users who can get interested in further actions, possibly contributing to a new project together with influential users. In this paper, we analyze the effect of contribution activities on gaining influence in this and comparable networks that provide users the functionality and aims for reaching collaborative goals on the Web. For this purpose, we present an empirical approach to identify the top influential users by using network features and contribution characteristics, which we find in existing and newly collected data set. We find that early adopter dynamics exist in the GitHub community, where early adopters have more followers in the end as expected. However, we also see counterexamples that arise due to the social networking behavior of late adopters, and due to the aging effect of older repositories and users. We publicly share the source code and the data sets for reproducing our paper.

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Section: Related Workmentioning

confidence: 99%