The last decade has witnessed the rapid growth of open source software~(OSS). Still, all contributors may find it difficult to assimilate into OSS community even they are enthusiastic to make contributions. We thus suggest that role recommendation may benefit both the users and developers, i.e., once we are able to make successful role recommendation for those in need, it may dramatically contribute to the productivity of developers and the enthusiasm of users, thus further boosting OSS projects' development. Motivated by this potential, we study the role recommendation from email data via network embedding methods. In this paper, we introduce time-series snapshot network~(TSSN) which is a mixture network to model the interactions among users and developers. Based on the established TSSN, we perform temporal biased walk~(TBW) to automatically capture both temporal and structural information of the email network, i.e., the behavioral similarity between individuals in the OSS email network. Experiments on ten Apache datasets demonstrate that the proposed TBW significantly outperforms a number of advanced random walk based embedding methods, leading to the state-of-the-art recommendation performance.

中文翻译：

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时间序列快照网络作为OSS中角色推荐的新模型

过去十年见证了开源软件~（OSS）的快速增长。尽管如此，所有贡献者可能会发现即使他们热衷于做出贡献也很难融入 OSS 社区。因此，我们认为角色推荐对用户和开发者都有利，即一旦我们能够成功地为有需要的人推荐角色，它可能会极大地提高开发者的生产力和用户的积极性，从而进一步推动 OSS 项目的发展。 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ' 发展。受这种潜力的推动，我们通过网络嵌入方法研究了来自电子邮件数据的角色推荐。在本文中，我们介绍了时间序列快照网络~（TSSN），它是一种混合网络，用于对用户和开发人员之间的交互进行建模。基于已建立的 TSSN，我们执行时间偏置步行~（TBW）以自动捕获电子邮件网络的时间和结构信息，即OSS电子邮件网络中个体之间的行为相似性。在十个 Apache 数据集上的实验表明，所提出的 TBW 显着优于许多基于随机游走的高级嵌入方法，从而实现了最先进的推荐性能。