Problem
Research questions and friction points this paper is trying to address.
Public Event Forecasting
Dynamic Interaction Network
Participant Growth
Event Evolution
A Network-driven Framework for Public Event Forecasting via Dynamic Interaction Network Evolution
🤖 AI Summary
This study addresses the insufficient modeling of dynamic interaction mechanisms in public event evolution prediction by proposing the Auto-IBDLM framework. This approach models events as dynamic interaction networks, integrating network science-inspired hybrid representation learning with GRU-based temporal modules to enable automatic transformation of structural features into a compact latent space and participant growth forecasting. Experiments across thirteen real-world datasets demonstrate that the model achieves an accuracy exceeding 97%, significantly outperforming existing methods. By combining high precision, strong generalization capability, and interpretability, this work effectively enhances predictive performance for public event evolution, offering a robust solution for capturing complex dynamic interactions in evolving social systems.
📝 Abstract
Effective public event forecasting is essential for intelligent service systems, enabling proactive risk management, adaptive resource allocation, and timely decision-making. In many real-world scenarios, the evolution of public events is driven by dynamic interactions among participants. Motivated by this observation, this paper proposes auto-ibDLM, a network-driven deep learning framework that represents events as dynamic interaction networks and predicts public event evolution through participant growth forecasting. The proposed framework adopts a hybrid representation learning strategy that first represents network evolution using network science-informed structural metrics and subsequently transforms the resulting structural feature vectors into compact and robust latent representations through an auto-learning layer. A GRU-based temporal forecasting module is then employed to capture temporal dependencies and predict future participant growth. Extensive experiments on 13 real-world public event datasets and two publicly available dynamic network datasets demonstrate that auto-ibDLM consistently outperforms representative state-of-the-art methods in both forecasting accuracy and generalization capability, achieving over 97% accuracy in public event forecasting. Comprehensive experimental analyses further validate the effectiveness of the proposed hybrid representation learning strategy and demonstrate its representation-level interpretability. These results indicate that auto-ibDLM provides an effective and practical solution for intelligent public event forecasting.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Interaction Network
Hybrid Representation Learning
Network Science-informed Metrics
Participant Growth Forecasting
auto-ibDLM
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