III: Medium: Collaborative Research: KMELIN: Knowledge Mining and Embedding Learning for Complex Dynamic Information Networks

III：媒介：协作研究：KMELIN：复杂动态信息网络的知识挖掘和嵌入学习

• 批准号: 1763452
• 负责人: Xingquan Zhu
• 金额: $ 60万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763452&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; KMELIN

Complex dynamic information networks (CDINs) consist of data objects that are highly correlated with a variety of dependency relationships, such as patient-physician interactions or patient-medication-insurance claims. Each data object as a CDIN node has rich contents, such as biometric information of a patient, disease symptoms, or hospital logistics. Data objects and their relationships also continuously evolve and change. Many health, social, physical, and biological systems share the CDIN essence that the multifaceted and dynamic nature of individual nodes imposes significant challenges for modeling a complex and evolving network as a whole. Although data relationships are becoming rich and comprehensive than ever, existing systems are mostly relational-database driven, and cannot integrate complex relationships of networked data for Big Data analytics.This project aims to design a knowledge mining and embedding learning platform for CDINs that will (1) extract and represent complex structure and rich-content information in the health domain as a CDIN; (2) perform knowledge mining, including clustering and classification, on CDIN networks; (3) enable feature embedding learning with CDINs, so the users can interact with CDINs for content access, and (4) provide a prototype system for hospital re-admission decision support. The spectrum of the methods from the project will not only enrich algorithms and solutions for mining complex structure and rich content networks, as opposed to static networks, but also shift existing health information systems from traditional databases towards becoming network centered systems.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.

复杂动态信息网络（CDIN）由与各种依赖关系高度相关的数据对象组成，例如患者-医生交互或患者-药物-保险索赔。作为CDIN节点的每个数据对象具有丰富的内容，例如患者的生物特征信息、疾病症状或医院后勤。数据对象及其关系也在不断发展和变化。许多健康、社会、物理和生物系统都具有CDIN的本质，即单个节点的多面性和动态性给整个复杂和不断发展的网络建模带来了重大挑战。本项目旨在设计一个面向CDIN的知识挖掘和嵌入式学习平台，该平台将（1）提取并表示健康领域中结构复杂、内容丰富的信息，作为CDIN;（2）在CDIN网络上进行知识挖掘，包括聚类和分类;（3）支持CDIN的特征嵌入学习，使用户可以与CDIN交互进行内容访问;（4）提供一个用于医院再入院决策支持的原型系统。该项目的方法不仅丰富了挖掘复杂结构和丰富内容网络的算法和解决方案，而不是静态网络，该奖项反映了美国国家科学基金会的法定使命，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进行评估来支持的搜索.

项目成果

Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning

• DOI: 10.1145/3357384.3358122
• 发表时间: 2019-11
• 期刊: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
• 作者: Man Wu;Shirui Pan;Lan Du;I. Tsang;Xingquan Zhu;Bo Du

Search Efficient Binary Network Embedding

• DOI: 10.1145/3436892
• 发表时间: 2019-01
• 期刊: ACM Transactions on Knowledge Discovery from Data (TKDD)
• 作者: Daokun Zhang;Jie Yin;Xingquan Zhu;Chengqi Zhang

MedFroDetect: Medicare Fraud Detection with Extremely Imbalanced Class Distributions

MedFroDetect：类别分布极其不平衡的医疗保险欺诈检测

• 发表时间: 2020
• 期刊: The Thirty-Third International FLAIRS Conference (FLAIRS-32
• 作者: Su, Yuping;Zhu, Xingquan;Dong, Bei;Zhang, Yumei;Wu, Xiaojun Wu
• 通讯作者: Wu, Xiaojun Wu

Deep Structure Learning for Fraud Detection

• DOI: 10.1109/icdm.2018.00072
• 发表时间: 2018-11
• 期刊: 2018 IEEE International Conference on Data Mining (ICDM)
• 作者: Haibo Wang;Chuan Zhou;Jia Wu;Weizhen Dang;Xingquan Zhu;Jilong Wang

User Response Prediction in Online Advertising

• DOI: 10.1145/3446662
• 发表时间: 2022-04-01
• 期刊: ACM COMPUTING SURVEYS
• 影响因子: 16.6
• 作者: Gharibshah，Zhabiz;Zhu，Xingquan
• 通讯作者: Zhu，Xingquan