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

在美国，各种阿片类药物在很大程度上助长了毁灭性和致命的阿片类药物流行。不幸的是，在巨大利润的推动下，类阿片贩运与现代技术共同发展，例如，社交媒体平台被用于营销和销售包括类阿片在内的非法药物，这引起了公共卫生机构和执法部门越来越多的关注。由于在线阿片类药物贩运活动灵活且具有弹性，因此需要新的技术来有效检测阿片类药物交易，以促进积极主动的应对战略。通过提高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，对动态多模态数据进行建模和分析，以打击在线阿片类药物贩运，从而帮助打击阿片类药物流行。这项研究将为联邦和州政府、公共卫生机构、执法部门和地方社区提供一个概念性框架，以制定积极主动的战略，建立一个无毒品的世界——一次一个社区。通过采用新颖的学科观点，这项探索性但具有变革性的高风险高回报工作将涉及制定打击在线阿片类药物贩运的综合框架的完全不同的方法。这项研究将有三个关键组成部分。首先，该团队将提出一种新的异构时间图（HTG）来全面建模和抽象社交媒体上随时间变化的多模态帖子和关系信息。其次，基于构建的HTG，研究团队将开发一种创新的图形转换器来学习用于检测阿片类药物贩运者的用户表示。第三，为了解决缺乏足够的标记数据用于模型训练的挑战，团队将进一步开发一种新的元学习算法，通过将无监督图结构和少量有监督训练数据结合起来更新模型。这将使该模型能够快速适应新的任务，例如仅使用少量样本和训练迭代就可以在社交媒体上识别新型交易的阿片类药物及其贩运者。为检测在线阿片类药物贩运活动制定的整体框架将对解决我们社会面临的严重的国家阿片类药物流行病产生重大影响。这项研究将有利于数据挖掘和机器学习社区，以及公共卫生、流行病学、社会和行为科学等多学科领域。该项目的成果将向公众开放并广泛分发。该项目将通过新课程开发、弱势群体的参与和学生辅导活动，将研究与教育结合起来。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。

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.