CRII: III: Learning Dynamic Graph-based Precursors for Event Modeling

从流行病爆发到内乱，涉及大量人口的社会事件往往深刻影响人们的生活并造成经济负担。预测这些事件，同时提供上下文分析，有助于社会科学家和卫生从业人员解释和研究人类社会。尽管许多现有的研究努力致力于预测社会事件，但考虑到这些事件背后的实体、行动和地点之间的潜在联系，为预测提供结构化的解释仍然有限。该项目提出了一种在预测事件时识别和组织多种类型前体的新范式。它识别随着事件的发展而变化的实体之间的关系，并研究对事件隐藏的地理影响。实体关系和地理联系都用动态图表示。在图中组织事件前体大大降低了理解非结构化输入数据的复杂性，并为事件预测提供了可解释的摘要。这项工作将涉及课程课程开发等教育活动；培养研究生、本科生和高中生；鼓励妇女和少数群体参与学术研究；以及向公众传播成果，如软件和数据集。为了实现这些目标，本项目将集成多个数据源，并在建模事件时分析复杂的分层特征。尽管各种在线数据已被用于分析和预测社会事件，但它也提出了新的挑战，例如：(1)考虑数据集中的动态关系；(2)利用异构数据集保存和学习复杂的知识结构；(3)确保预测和决策结果的可解释性。本项目将通过以下方式解决这些挑战：(i)通过学习统一的多层次语义编码来整合多源数据；（ii）它将通过关注循环学习过程中的分层文本结构来识别历史关键语义；（iii）它将结合局部动态图形模式和全球影响图形模式，为事件预测提供解释。具体的研究目标将与一套广泛的评估计划相辅相成，包括对真实事件记录的回顾性评估和评估事件前体图形可视化的用户调查。项目结果，包括基于图形的经验数据、预测评估工具和用于分析事件的开源软件，将与计算机科学研究界以及计算医疗保健和社会科学领域的利益相关者共享。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

From epidemic outbreaks to civil strife, societal events that involve large populations often deeply affect people’s lives and cause economic burden. Forecasting these events while providing context analysis helps social scientists and health practitioners to interpret and study human societies. Although many existing research efforts strive to forecast societal events, providing structured explanations for prediction is still limited given the underlying connections among entities, actions, and locations behind these events. This project presents a novel paradigm of identifying and organizing multiple types of precursors while predicting events. It identifies changing relations among entities as events evolve and studies the hidden geographical influence on events. Both entity relations and geographical connections are represented by dynamic graphs. Organizing event precursors in graphs greatly reduces the complexity of comprehending unstructured input data and delivers interpretable summarizations for event prediction. This work will involve educational activities such as development of course curriculum; training of graduate, undergraduate, and high-school students; encouraging participation of women and minority groups in academic research; and dissemination of outcomes such as software and datasets for the general public.To achieve these goals, this project will integrate multiple data sources and analyze complex hierarchical features in modeling events. Although a variety of online data has been utilized to analyze and predict societal events, it also raises new challenges such as: (1) accounting for dynamic relationships within data sets; (2) preserving and learning complex knowledge structures with heterogeneous data sets; and (3) ensuring interpretable results for predictions and decision making. This project will address the challenges in the following ways: (i) it will integrate multi-source data by learning a unified multi-level semantic encoding; (ii) it will identify historical key semantics by paying attention to hierarchical text structures in a recurrent learning process; (iii) it will provide explanations for event prediction by incorporating local dynamic graph patterns and global influence graph patterns. The specific research aims will be complemented with an extensive set of evaluation plans including a retrospective evaluation on real-word event records and a user survey to evaluate graph visualizations of event precursors. The project results, including graph based empirical data, predictive evaluation tools, and open source software for analyzing events, will be shared with computer science research community and stakeholders in computational healthcare, and social science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.