SEERa: A Framework for Community Prediction

Online user communities exhibit distinct temporal dynamics in response to popular topics or breaking events. Despite abundant community detection libraries, there is yet to be one that provides access to the possible user communities in future time intervals. To bridge this gap, we contribute SEERa, an open-source end-to-end community prediction framework to identify future user communities in a text streaming social network. SEERa incorporates state-of-the-art temporal graph neural networks to model inter-user topical affinities at each time interval via streams of temporal graphs. This all takes place while users' topics of interest and hence their inter-user topical affinities are changing over time. SEERa predicts yet-to-be-seen user communities on the final positions of users' vectors in the latent space. Notably, our framework serves as a one-stop-shop to future user communities for Social Information Retrieval and Social Recommendation systems. While there are strong research papers on the community prediction problem, SEERa is the first framework to be publicly released for this purpose.