Modeling social coding dynamics with sampled historical data

Neda Hajiakhoond Bidoki, Madeline Schiappa, Gita Sukthankar, Ivan Garibay

Abstract: The aim of our research is to forecast the propagation of information related to cybersecurity threats and software vulnerabilities on social coding platforms such as GitHub. Users on social coding platforms exhibit repetitive behavior patterns that can be leveraged to predict trends in network evolution. These patterns exhibit greater consistency within a single community of users; hence global data distributions can be more accurately modeled by composing community data distributions. A wise sampling approach based on the identification of similarities between the historical and predicted patterns in social behavior can be used to augment the performance of other approaches in order to create an at scale simulation. This article compares two different strategies for predicting network evolution with sampled historical data on GitHub. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over three different datasets.