SEERa: A Framework for Community Prediction
Authors: 
Soroush Ziaeinejad, Saeed Samet, and 
Hossein FaniAuthors Info & Claims
Soroush Ziaeinejad, Saeed Samet, and Hossein FaniAuthors Info & ClaimsCIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
Pages 4762 - 4766
Abstract
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.
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Published: 17 October 2022
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- Natural Sciences and Engineering Research Council of Canada (NSERC)
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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management
October 17 - 21, 2022
GA, Atlanta, USA
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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
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