DeepFork: Supervised Prediction of Information Diffusion in GitHub
This work addresses the challenge of understanding and predicting information spread on GitHub, which is important for businesses and communities using the platform, but it appears incremental as it applies a supervised learning approach to a specific domain.
The paper tackles the problem of predicting information diffusion in GitHub's social network by developing DeepFork, a supervised deep neural network model that uses node and topological features, and it outperforms other machine learning models in detecting diffusion through link prediction.
Information spreads on complex social networks extremely fast, in other words, a piece of information can go viral within no time. Often it is hard to barricade this diffusion prior to the significant occurrence of chaos, be it a social media or an online coding platform. GitHub is one such trending online focal point for any business to reach their potential contributors and customers, simultaneously. By exploiting such software development paradigm, millions of free software emerged lately in diverse communities. To understand human influence, information spread and evolution of transmitted information among assorted users in GitHub, we developed a deep neural network model: DeepFork, a supervised machine learning based approach that aims to predict information diffusion in complex social networks; considering node as well as topological features. In our empirical studies, we observed that information diffusion can be detected by link prediction using supervised learning. DeepFork outperforms other machine learning models as it better learns the discriminative patterns from the input features. DeepFork aids in understanding information spread and evolution through a bipartite network of users and repositories i.e., information flow from a user to repository to user.