III: Small: Collaborative Research: A General Feature Learning Framework for Dynamic Attributed Networks

III：小：协作研究：动态属性网络的通用特征学习框架

• 批准号: 1718840
• 负责人: Xia Hu
• 金额: $ 25.19万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718840&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; General

Attributed networks are those networks which are associated with a rich set of attributes. For example, in online social networks, users post messages related to what they are experiencing, which can be represented as a series of word attributes; in health care systems, providers are networked with each other given their shared patients, and each provider has profile information and may submit insurance claims as attribute information. Feature learning aims at seeking effective representations of data instances in preparing the attributed networks for various data mining tasks. Feature learning algorithms, including feature extraction and feature selection, have been intensively studied in the literature. While most existing studies focused on static, pure and shallow networks, this project aims to develop novel feature learning algorithms for dynamic attributed networks. The output of the project will be a series of feature learning algorithms, including shallow and deep network embedding, and feature selection, specifically designed for dynamic attributed networks. The developed algorithms, as well as their corresponding theoretical understandings, are expected to significantly advance data-driven social computing and health informatics. The goal of this project is to develop a novel feature learning framework for dynamic attributed networks, which consists of network embedding and corresponding deep architectures, as well as feature selection algorithms. The feature learning framework is feasible to effectively and efficiently address data challenges raised by dynamic attributed networks from various aspects. Specifically, this project aims to achieve the research goal through three primary research objectives: (1) performing dynamic network embedding under challenging scenarios, including the limited label information, heterogeneous feature spaces, and scalability of the data; (2) designing deep architectures for dynamic network embedding on various types of attributed networks; and (3) developing feature selection methods, by modeling dynamic attributed networks with link weights and cross-media links, to further enable interpretability in network analytics. In addition, this project will incorporate the research problems in a new curriculum, and it will also allow the PIs to continue the ongoing efforts to provide research opportunities to undergraduate and underrepresented students.

属性网络是与丰富属性相关联的网络。例如，在在线社交网络中，用户发布了与他们所经历的信息有关的消息，可以将其表示为一系列单词属性；在卫生保健系统中，鉴于他们的共享患者，提供者相互联系，每个提供商都有个人资料信息，并且可以将保险索赔作为属性信息提交。功能学习旨在寻求数据实例的有效表示，以准备各种数据挖掘任务的属性网络。文献中已经对特征学习算法（包括特征提取和特征选择）进行了深入研究。尽管大多数现有研究都集中在静态，纯和浅网络上，但该项目旨在为动态归因网络开发新颖的功能学习算法。该项目的输出将是一系列特征学习算法，包括浅网络和深网嵌入以及特征选择，专门为动态归因网络设计。预计开发的算法及其相应的理论理解将显着提高数据驱动的社会计算和健康信息学。该项目的目的是为动态归因网络开发一个新颖的功能学习框架，该框架由网络嵌入和相应的深度体系结构以及功能选择算法组成。功能学习框架是可行的，可以有效，有效地解决来自各个方面的动态归因网络所提出的数据挑战。具体而言，该项目旨在通过三个主要的研究目标来实现研究目标：（1）在具有挑战性的情况下执行动态网络嵌入，包括有限的标签信息，异质性特征空间和数据的可扩展性； （2）设计深层体系结构，以嵌入各种类型的归因网络上的动态网络； （3）开发特征选择方法，通过建模具有链路权重和跨媒体链接的动态归因网络，以进一步启用网络分析中的可解释性。此外，该项目将将研究问题纳入新的课程中，还将允许PIS继续努力为本科和代表性不足的学生提供研究机会。