A two-stage model leveraging friendship network for community evolution prediction in interactive networks

• URL: http://arxiv.org/abs/2503.15788v1
• Date: Thu, 20 Mar 2025 02:05:36 GMT
• Title: A two-stage model leveraging friendship network for community evolution prediction in interactive networks
• Authors: Yanmei Hu, Yihang Wu, Biao Cai,
• Abstract summary: We propose a two-stage model that predicts the type and extent of community evolution.<n>Our model unifies multi-class classification for evolution type and regression for evolution extent within a single framework.<n> Experimental results on three datasets show the significant superiority of the proposed model over other models.
• Score: 2.075891315372943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interactive networks representing user participation and interactions in specific "events" are highly dynamic, with communities reflecting collective behaviors that evolve over time. Predicting these community evolutions is crucial for forecasting the trajectory of the related "event". Some models for community evolution prediction have been witnessed, but they primarily focused on coarse-grained evolution types (e.g., expand, dissolve, merge, split), often neglecting fine-grained evolution extents (e.g., the extent of community expansion). Furthermore, these models typically utilize only one network data (here is interactive network data) for dynamic community featurization, overlooking the more stable friendship network that represents the friendships between people to enrich community representations. To address these limitations, we propose a two-stage model that predicts both the type and extent of community evolution. Our model unifies multi-class classification for evolution type and regression for evolution extent within a single framework and fuses data from both interactive and friendship networks for a comprehensive community featurization. We also introduce a hybrid strategy to differentiate between evolution types that are difficult to distinguish. Experimental results on three datasets show the significant superiority of the proposed model over other models, confirming its efficacy in predicting community evolution in interactive networks.

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