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，例如患者与医生的交互或患者-药物-保险索赔。作为CDIN节点的每个数据对象都具有丰富的内容，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，即各个节点的多面性和动态性给复杂且不断发展的网络建模带来了巨大的挑战。尽管数据关系比以往任何时候都变得丰富和全面，但现有系统大多是关系数据库驱动的，无法集成网络数据的复杂关系进行大数据分析。该项目旨在为 CDIN 设计一个知识挖掘和嵌入学习平台，该平台将（ 1）提取健康领域复杂结构、内容丰富的信息并将其表示为CDIN； (2) 在CDIN网络上进行知识挖掘，包括聚类和分类； (3) 通过 CDIN 启用特征嵌入学习，以便用户可以与 CDIN 交互以进行内容访问，以及 (4) 为医院重新入院决策支持提供原型系统。该项目的方法范围不仅将丰富用于挖掘复杂结构和丰富内容网络（而不是静态网络）的算法和解决方案，而且还将现有的健康信息系统从传统数据库转变为以网络为中心的系统。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Relation Structure-Aware Heterogeneous Graph Neural Network

关系结构感知异构图神经网络

• DOI: 10.1109/icdm.2019.00203
• 发表时间: 2019-11
• 期刊: Proc. of the 19th IEEE International Conference on Data Mining (ICDM-2019
• 作者: Zhu, Shichao;Zhou, Chuan;Pan, Shirui;Zhu, Xingquan;Wang, Bin
• 通讯作者: Wang, Bin

Multi-Label Graph Convolutional Network Representation Learning

多标签图卷积网络表示学习

• DOI: 10.1109/tbdata.2020.3019478
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Shi, Min;Tang, Yufei;Zhu, Xingquan;Liu, Jianxun
• 通讯作者: Liu, Jianxun

Network Representation Learning: A Survey

网络表征学习：一项调查

• DOI: 10.1109/tbdata.2018.2850013
• 发表时间: 2017-12-04
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Daokun Zhang;Jie Yin;Xingquan Zhu;Chengqi Zhang
• 通讯作者: Chengqi Zhang

Discriminative Sample Generation for Deep Imbalanced Learning

深度不平衡学习的判别样本生成

• DOI: 10.24963/ijcai.2019/334
• 发表时间: 2019-08-01
• 作者: Ting Guo;Xingquan Zhu;Yang Wang;Fang Chen
• 通讯作者: Fang Chen

SINE: Scalable Incomplete Network Embedding

SINE：可扩展的不完整网络嵌入

• DOI: 10.1109/icdm.2018.00089
• 发表时间: 2018-10-16
• 期刊: 2018 IEEE International Conference on Data Mining (ICDM)
• 作者: Daokun Zhang;Jie Yin;Xingquan Zhu;Chengqi Zhang
• 通讯作者: Chengqi Zhang