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

基本信息

  • 批准号:
    2203262
  • 负责人:
  • 金额:
    $ 15万
  • 依托单位:
  • 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:
    2021-11-01 至 2024-09-30
  • 项目状态:
    已结题

项目摘要

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.
毁灭性和致命的阿片类药物流行病在很大程度上是由美国的各种阿片类药物推动的。不幸的是,在巨大利润的驱动下,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。由于在线阿片类贩运活动灵活而有弹性,因此需要新的技术来有效地检测阿片类交易,以促进积极的应对战略。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来建模和分析动态多模态数据,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病。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制定一个概念框架,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。通过采用新的学科视角,这项探索性的、但具有变革性的、高风险高回报的工作将涉及制定打击网上阿片类药物贩运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态图(HTG),以全面建模和抽象社交媒体上随时间推移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Transformer,以学习用于阿片类药物贩运者检测的用户表示。第三,为了解决缺乏足够的标记数据进行模型训练的挑战,该团队将进一步开发一种新的元学习算法,通过加入无监督图结构和少量的监督训练数据来更新模型。这将使该模型能够快速适应新的任务,例如识别社交媒体上的新型阿片类药物及其贩运者,只需使用少量样本和训练迭代。为侦查网上类阿片贩运活动而制定的整体框架将对解决我们社会面临的严重的全国性类阿片流行病产生重大影响。该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。该项目将通过新颖的课程开发、代表性不足的群体的参与和学生指导活动将研究与教育结合起来。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量(23)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Back-Propagating System Dependency Impact for Attack Investigation
攻击调查的反向传播系统依赖性影响
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Fang, Pengcheng;Gao, Peng;Liu, Changlin;Ayday, Erman;Jee, Kangkook;Wang, Ting;Ye, Yanfang;Liu, Zhuotao;Xiao, Xusheng
  • 通讯作者:
    Xiao, Xusheng
Heterogeneous Graph Structure Learning for Graph Neural Networks
  • DOI:
    10.1609/aaai.v35i5.16600
  • 发表时间:
    2021-05
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jianan Zhao;Xiao Wang;C. Shi;Binbin Hu;Guojie Song;Yanfang Ye
  • 通讯作者:
    Jianan Zhao;Xiao Wang;C. Shi;Binbin Hu;Guojie Song;Yanfang Ye
Adapting Distilled Knowledge for Few-Shot Relation Reasoning over Knowledge Graphs
采用蒸馏知识进行知识图上的少样本关系推理
Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach
Adaptive Kernel Graph Neural Network
  • DOI:
    10.1609/aaai.v36i6.20664
  • 发表时间:
    2021-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mingxuan Ju;Shifu Hou;Yujie Fan;Jianan Zhao;Liang Zhao;Yanfang Ye
  • 通讯作者:
    Mingxuan Ju;Shifu Hou;Yujie Fan;Jianan Zhao;Liang Zhao;Yanfang Ye
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Yanfang Ye其他文献

Classifying construction site photos for roof detection
对施工现场照片进行分类以进行屋顶检测
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Madhuri Siddula;F. Dai;Yanfang Ye;Jianping Fan
  • 通讯作者:
    Jianping Fan
Efficacy and safety of cadonilimab (PD-1/CTLA-4 bispecific) in combination with chemotherapy in anti-PD-1-resistant recurrent or metastatic nasopharyngeal carcinoma: a single-arm, open-label, phase 2 trial
  • DOI:
    10.1186/s12916-025-03985-4
  • 发表时间:
    2025-03-11
  • 期刊:
  • 影响因子:
    8.300
  • 作者:
    Yaofei Jiang;Weixin Bei;Lin Wang;Nian Lu;Cheng Xu;Hu Liang;Liangru Ke;Yanfang Ye;Shuiqing He;Shuhui Dong;Qin Liu;Chuanrun Zhang;Xuguang Wang;Weixiong Xia;Chong Zhao;Ying Huang;Yanqun Xiang;Guoying Liu
  • 通讯作者:
    Guoying Liu
THERMO-SENSITIVE SPIKELET DEFECTS 1 acclimatizes rice spikelet initiation and development to high temperature
热敏小穗缺陷 1 使水稻小穗的萌生和发育适应高温
  • DOI:
    10.1093/plphys/kiac576
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    7.4
  • 作者:
    Zhengzheng Cai;Gang Wang;Jieqiong Li;Lan Kong;Weiqi Tang;Xuequn Chen;Xiaojie Qu;Chenchen Lin;Yulin Peng;Yang Liu;Zhanlin Deng;Yanfang Ye;Weiren Wu;Yuanlin Duan
  • 通讯作者:
    Yuanlin Duan
ISMCS: An intelligent instruction sequence based malware categorization system
ISMCS:基于智能指令序列的恶意软件分类系统
Survival neural networks for time-to-event prediction in longitudinal study
用于纵向研究中事件发生时间预测的生存神经网络
  • DOI:
    10.1007/s10115-020-01472-1
  • 发表时间:
    2020-05
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    张健飞;陈黎飞;Yanfang Ye;郭躬德;Rongbo Chen;Alain Vanasse;王声瑞
  • 通讯作者:
    王声瑞

Yanfang Ye的其他文献

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{{ truncateString('Yanfang Ye', 18)}}的其他基金

EAGER: A New Explainable Multi-objective Learning Framework for Personalized Dietary Recommendations against Opioid Misuse and Addiction
EAGER:一种新的可解释的多目标学习框架,用于针对阿片类药物滥用和成瘾的个性化饮食建议
  • 批准号:
    2334193
  • 财政年份:
    2023
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
III: Small: A New Machine Learning Paradigm Towards Effective yet Efficient Foundation Graph Learning Models
III:小型:一种新的机器学习范式,实现有效且高效的基础图学习模型
  • 批准号:
    2321504
  • 财政年份:
    2023
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
D-ISN: An AI-augmented Framework to Detect, Disrupt, and Dismantle Opioid Trafficking Networks
D-ISN:用于检测、破坏和拆除阿片类药物贩运网络的人工智能增强框架
  • 批准号:
    2146076
  • 财政年份:
    2022
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
CAREER: Securing Cyberspace: Gaining Deep Insights into the Online Underground Ecosystem
职业:保护网络空间:深入了解在线地下生态系统
  • 批准号:
    2203261
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Continuing Grant
EAGER: An AI-driven Paradigm for Collective and Collaborative Community Resilience in the COVID-19 Era and Beyond
EAGER:COVID-19 时代及以后的集体和协作社区复原力的人工智能驱动范式
  • 批准号:
    2209814
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
III: Small: Mining Heterogeneous Network Built from Multiple Data Sources to Reduce Opioid Overdose Risks
III:小型:挖掘由多个数据源构建的异构网络以减少阿片类药物过量风险
  • 批准号:
    2214376
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
III: Medium: A Data-driven and AI-augmented Framework for Collaborative Decision Making to Combat Infectious Disease Outbreaks
III:媒介:数据驱动和人工智能增强的框架,用于对抗传染病爆发的协作决策
  • 批准号:
    2217239
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Continuing Grant
CICI: SSC: SciTrust: Enhancing Security for Modern Software Programming Cyberinfrastructure
CICI:SSC:SciTrust:增强现代软件编程网络基础设施的安全性
  • 批准号:
    2218762
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
III: Medium: A Data-driven and AI-augmented Framework for Collaborative Decision Making to Combat Infectious Disease Outbreaks
III:媒介:数据驱动和人工智能增强的框架,用于对抗传染病爆发的协作决策
  • 批准号:
    2107172
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Continuing Grant
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
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant

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