基于动态交互网络演化的公共事件预测网络驱动框架
A Network-driven Framework for Public Event Forecasting via Dynamic Interaction Network Evolution
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中文总结 AI 辅助
该研究针对公共事件预测问题，提出网络驱动的深度学习框架auto-ibDLM，采用混合表示学习策略结合GRU模块，在多数据集上优于现有方法，准确率超97%，为智能公共事件预测提供有效方案。
中文摘要 AI 辅助
有效的公共事件预测对智能服务系统至关重要，可实现主动风险管理、自适应资源分配和及时决策。在许多现实场景中，公共事件的演化由参与者之间的动态交互驱动。基于这一观察，本文提出auto-ibDLM，一种网络驱动的深度学习框架，该框架将事件表示为动态交互网络，并通过参与者增长预测来预测公共事件演化。所提框架采用混合表示学习策略：首先使用网络科学启发的结构指标表示网络演化，随后通过自动学习层将生成的结构特征向量转换为紧凑且鲁棒的潜在表示。接着采用基于GRU的时间预测模块捕获时间依赖关系并预测未来参与者增长。在13个现实公共事件数据集和两个公开动态网络数据集上的大量实验表明，auto-ibDLM在预测准确性和泛化能力上始终优于代表性的最先进方法，在公共事件预测中准确率超过97%。全面的实验分析进一步验证了所提混合表示学习策略的有效性，并证明其具有表示级可解释性。这些结果表明，auto-ibDLM为智能公共事件预测提供了一种有效且实用的解决方案。
英文摘要
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.
