Abstract
Social collaborative coding is a popular trend in software development, and such platforms as GitHub provide rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection, and collaboration. Thus, identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue, and watch are extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real-world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods, and multiplex ranking method.
Highlights
Open source software community is a main driven force of innovations, and plenty of software developers collaborate on millions of open source software projects, among which are many popular software projects that drive the innovations of different fields [1, 2]
We focus on modeling multiple interactions between developers and projects as a multiplex bipartite network and propose a new ranking method based on it in an iterative and mutually enhanced way
We propose a new ranking model called M-BiRank on multiplex bipartite network which takes into account the mutual reinforcement between different types of nodes as well as different layers
Summary
Open source software community is a main driven force of innovations, and plenty of software developers collaborate on millions of open source software projects, among which are many popular software projects that drive the innovations of different fields [1, 2]. In open source software community, developers from different areas usually take the social collaborative coding paradigm and participate in different portions of a common. Much like the role of opinion leaders in social networks, influential developers and projects drive the technical trends and the prosperity of open source community. Existing work on influence analysis for open source software community mainly focused on applying traditional unipartite single layer graph ranking methods [7, 8], including PageRank [9] and HITS [10], many new graph ranking methods for more complex network structures, such as bipartite network [11, 12] and multiplex network [13, 14], have been proposed. Potential applications of influence analysis of open source software community would include service recommendations [15,16,17,18,19] and risk assessment [20]
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