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.

社交协作编码是软件开发中的一种流行趋势，而GitHub这样的平台为开发人员提供了丰富的社交和技术功能，使其可以通过多次交互在开源项目上进行协作。开发人员经常跟随流行的开发人员和项目进行学习，技术选择和协作。因此，确定受欢迎的开发人员和项目非常有意义。在本文中，我们提出了一个多重双向网络排名模型M-BiRank，以使用多个开发者-项目交互来共同对开发者和项目进行排名。首先，提取多个开发人员-项目交互，例如提交，发布和监视，并构建一个多元化的开发人员-项目双向网络。其次，从该多重二分网络中选择一个随机层，并使用BiRank为开发人员和项目计算初始排名分数。最后，初始排名分数会扩散到其他层，并考虑相互加强，以迭代方式计算不同层级的开发人员和项目的排名分数。在真实的GitHub数据集上进行的实验表明，M-BiRank优于度中心性，传统的单层排序方法和多路复用排序方法。