EAGER: A Holistic Heterogeneous Temporal Graph Transformer Framework with Meta-learning to Combat Opioid Epidemic

EAGER：利用元学习对抗阿片类药物流行病的整体异构时间图转换器框架

• 批准号: 2140785
• 负责人: Yanfang Ye
• 金额: $ 15万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140785&HistoricalAwards=false
• 关键词: EAGER; Holistic; Heterogeneous; Temporal; Graph

The devastating and lethal opioid epidemic has largely been fueled with various opioids in the United States. Unfortunately, driven by considerable profits, opioid trafficking has co-evolved with modern technologies, such as, social media platforms have been utilized for marketing and selling illicit drugs including opioids, which has attracted increasing attention from both public health agencies and law enforcement. As online opioid trafficking activities are nimble and resilient, it calls for novel techniques to effectively detect opioid trades to facilitate proactive response strategies. By advancing capabilities of machine learning and data science, the goal of this project is to design and develop a holistic framework to model and analyze dynamic multi-modal data to fight against online opioid trafficking and, thus, help combat opioid epidemic. This research will enable a conceptual framework for the federal and state governments, public health agencies, law enforcement, and local communities to develop proactive strategies to build up a drug-free world - one community at a time. By engaging novel disciplinary perspectives, this exploratory, yet transformative, high risk-high payoff work will involve radically different approaches for the development of an integrated framework to combat online opioid trafficking. The research will have three key components. First, the team will propose a novel heterogeneous temporal graph (HTG) to comprehensively model and abstract multi-modal posts and relational information over time on social media. Second, based on the constructed HTG, the research team will develop an innovative graph transformer to learn user representations for opioid trafficker detection. Third, to tackle the challenge of lack of sufficient labeled data for model training, the team will further develop a new meta-learning algorithm by joining unsupervised graph structure and small amount of supervised training data to update the model. This will enable the model to quickly adapt to a new task, such as identifying a new type of traded opioid and its traffickers on social media, using only a few samples and training iterations. The developed holistic framework for the detection of online opioid trafficking activities will have significant impacts on addressing the critical national opioid epidemic facing our society. The research will be beneficial to data mining and machine learning communities, as well as multidisciplinary domains such as public health, epidemiology, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through novel curriculum development, participation of underrepresented groups, and student mentoring activities.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.

美国各种阿片类药物在很大程度上加剧了毁灭性和致命的阿片类药物流行。不幸的是，在大量利润的推动下，阿片类药物贩运与现代技术共同发展，例如，社交媒体平台已用于营销和销售包括阿片类药物在内的非法药物，这引起了公共卫生机构和执法部门的越来越多的关注。由于在线阿片类贩运活动是敏捷且有弹性的，因此它要求采用新颖的技术来有效地检测阿片类药物贸易，以促进主动的反应策略。通过推进机器学习和数据科学的能力，该项目的目标是设计和开发一个整体框架，以建模和分析动态多模式数据，以与在线阿片类贩运作斗争，从而有助于对抗阿片类药物的流行。这项研究将为联邦和州政府，公共卫生机构，执法部门和当地社区提供一个概念框架，以制定积极的策略来建立一个无毒的世界 - 一次一个社区。通过吸引新颖的纪律观点，这种探索性但具有变革性的高风险收益工作将涉及开发综合框架以打击在线在线阿片类药物贩运的截然不同的方法。该研究将有三个关键组成部分。首先，该团队将提出一个新颖的异质时间图（HTG），以全面建模和抽象的多模式帖子和随着时间的推移在社交媒体上的关系信息。其次，根据构建的HTG，研究团队将开发一个创新的图形变压器，以学习阿片类贩子检测的用户表示。第三，为了应对缺乏足够标记的数据进行模型培训的挑战，团队将通过加入无监督的图形结构和少量监督培训数据来进一步开发新的元学习算法以更新模型。这将使该模型能够快速适应一项新任务，例如在社交媒体上仅使用一些样本和培训迭代来确定一种新型的交易阿片类药物及其贩运者。开发的用于检测在线阿片类贩运活动的整体框架将对解决我们社会面临的关键国家阿片类药物流行产生重大影响。这项研究将对数据挖掘和机器学习社区以及诸如公共卫生，流行病学，社会和行为科学等多学科领域有益。该项目的结果将被公开访问和广泛分布。该项目将通过新的课程开发，代表性不足的团体的参与以及学生指导活动将研究与教育结合起来。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查标准来通过评估来支持的。