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

CRII：III：学习基于动态图的事件建模前体

• 批准号: 1948432
• 负责人: Yue Ning
• 金额: $ 17.5万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948432&HistoricalAwards=false
• 关键词: CRII; III; Learning; Dynamic; Graph

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.

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，同时提供背景分析，有助于社会科学家和卫生从业人员解释和研究人类社会。虽然许多现有的研究努力预测社会事件，提供结构化的解释预测仍然是有限的，因为这些事件背后的实体，行动和位置之间的潜在联系。该项目提出了一种新的模式，识别和组织多种类型的前兆，同时预测事件。它确定随着事件的发展而变化的实体之间的关系，并研究隐藏的地理对事件的影响。实体关系和地理连接都用动态图表示。在图形中组织事件前兆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。本项目将通过课程设置、研究生、大学生、高中生的培养、女性和少数民族参与学术研究、软件和数据集等成果向公众的传播等教育活动，整合多个数据源，分析事件建模中复杂的层次特征。 虽然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）解释数据集内的动态关系;（2）保存和学习具有异构数据集的复杂知识结构;以及（3）确保预测和决策的可解释结果。该项目将通过以下方式应对挑战：（i）通过学习统一的多层次语义编码来整合多源数据;（ii）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）通过结合局部动态图模式和全局影响图模式来解释事件预测。具体的研究目标将得到一套广泛的评估计划的补充，包括对真实事件记录的回顾性评估和用户调查，以评估事件前兆的图形可视化。该项目的成果，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社区和计算医疗保健和社会科学的利益相关者分享。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Incorporating Relational Knowledge in Explainable Fake News Detection

• DOI: 10.1007/978-3-030-75768-7_32
• 发表时间: 2021
• 作者: Kun Wu;Xu Yuan;Yue Ning

FairLP: Towards Fair Link Prediction on Social Network Graphs

FairLP：迈向社交网络图上的公平链接预测

• DOI: 10.1609/icwsm.v16i1.19321
• 发表时间: 2022
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Li, Yanying;Wang, Xiuling;Ning, Yue;Wang, Hui
• 通讯作者: Wang, Hui

Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

• DOI: 10.1609/aaai.v36i4.20380
• 发表时间: 2021-12
• 期刊: ArXiv
• 作者: Chang Lu;Tian Han;Yue Ning

Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

• DOI: 10.24963/ijcai.2021/486
• 发表时间: 2021-05
• 期刊: ArXiv
• 作者: Chang Lu;Chandan K. Reddy;Prithwish Chakraborty;Samantha Kleinberg;Yue Ning

Algorithmic fairness in computational medicine.

计算医学中的算法公平性。

• DOI: 10.1016/j.ebiom.2022.104250
• 发表时间: 2022-10
• 期刊: EBIOMEDICINE
• 影响因子: 11.1
• 作者: Xu, Jie;Xiao, Yunyu;Wang, Wendy Hui;Ning, Yue;Shenkman, Elizabeth A.;Bian, Jiang;Wang, Fei
• 通讯作者: Wang, Fei