DevRank: Mining Influential Developers In Github

TL;DR

This paper introduces DevRank, a novel method for ranking influential GitHub developers by modeling influence propagation in a heterogeneous network based on user behaviors like commits and follows, improving accuracy over existing algorithms.

Contribution

The paper proposes DevRank, a new influence ranking algorithm tailored for GitHub, utilizing heterogeneous network analysis to better capture developer influence.

Findings

DevRank outperforms existing link analysis algorithms in ranking accuracy.

Influence on GitHub can be effectively modeled through user behavior-based heterogeneous networks.

The method demonstrates improved identification of influential developers.

Abstract

As the social coding is becoming increasingly popular, understanding the influence of developers can benefit various applications, such as advertisement for new projects and innovations. However, most existing works have focused only on ranking influential nodes in non-weighted and homogeneous networks, which are not able to transfer proper importance scores to the real important node. To rank developers in Github, we define developer's influence on the capacity of attracting attention which can be measured by the number of followers obtained in the future. We further defined a new method, DevRank, which ranks the developers by influence propagation through heterogeneous network constructed according to user behaviors, including "commit" and "follow". Our experiment compares the performance between DevRank and some other link analysis algorithms, the results have shown that DevRank can improve the ranking accuracy.