DeepFork: Supervised Prediction of Information Diffusion in GitHub

TL;DR

DeepFork is a deep neural network model designed to predict information diffusion on GitHub by leveraging node and topological features, outperforming other models in link prediction tasks.

Contribution

The paper introduces DeepFork, a novel supervised deep learning approach for predicting information spread in GitHub's social network, incorporating user and repository features.

Findings

DeepFork outperforms other machine learning models in diffusion prediction.

Information diffusion can be effectively detected through link prediction.

The model captures patterns of information flow in bipartite user-repository networks.

Abstract

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.