  Software development is becoming increasingly open and collaborative with the
advent of platforms such as GitHub. Given its crucial role, there is a need to
better understand and model the dynamics of GitHub as a social platform.
Previous work has mostly considered the dynamics of traditional social
networking sites like Twitter and Facebook. We propose GitEvolve, a system to
predict the evolution of GitHub repositories and the different ways by which
users interact with them. To this end, we develop an end-to-end multi-task
sequential deep neural network that given some seed events, simultaneously
predicts which user-group is next going to interact with a given repository,
what the type of the interaction is, and when it happens. To facilitate
learning, we use graph based representation learning to encode relationship
between repositories. We map users to groups by modelling common interests to
better predict popularity and to generalize to unseen users during inference.
We introduce an artificial event type to better model varying levels of
activity of repositories in the dataset. The proposed multi-task architecture
is generic and can be extended to model information diffusion in other social
networks. In a series of experiments, we demonstrate the effectiveness of the
proposed model, using multiple metrics and baselines. Qualitative analysis of
the model's ability to predict popularity and forecast trends proves its
applicability.
