Time-Series Snapshot Network for Partner Recommendation: A Case Study on OSS

TL;DR

This paper introduces a novel time-series snapshot network and temporal biased walk method for partner recommendation in open source software communities, significantly improving link prediction accuracy using email interaction data.

Contribution

It presents the TSSN model and TBW method, which effectively capture temporal and structural information for improved partner recommendation in OSS.

Findings

TBW outperforms existing random walk methods.

Achieves state-of-the-art recommendation accuracy.

Validated on ten Apache datasets.

Abstract

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 partner recommendation across different roles may benefit both the users and developers, i.e., once we are able to make successful 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 model the partner recommendation as link prediction task 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.