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

属性网络是指那些与丰富的属性集相关联的网络。例如，在在线社交网络中，用户发布与他们正在经历的事情相关的消息，其可以被表示为一系列词属性;在健康护理系统中，提供者在给定他们共享的患者的情况下彼此联网，并且每个提供者具有简档信息并且可以提交保险索赔作为属性信息。特征学习的目的是寻求数据实例的有效表示，为各种数据挖掘任务准备属性网络。特征学习算法，包括特征提取和特征选择，已经在文献中被深入研究。虽然大多数现有的研究集中在静态，纯和浅层网络，这个项目的目的是开发新的特征学习算法的动态属性网络。该项目的成果将是一系列的特征学习算法，包括浅层和深层网络嵌入，以及专为动态属性网络设计的特征选择。所开发的算法及其相应的理论理解，预计将显着推进数据驱动的社会计算和健康信息学。该项目的目标是为动态属性网络开发一个新的特征学习框架，包括网络嵌入和相应的深度架构，以及特征选择算法。该特征学习框架能够从多个方面有效地解决动态属性网络带来的数据挑战。具体而言，本项目旨在通过三个主要研究目标来实现研究目标：（1）在具有挑战性的场景下执行动态网络嵌入，包括有限的标签信息，异构的特征空间和数据的可扩展性;（2）设计各种类型的属性网络上的动态网络嵌入的深度架构;（3）设计基于属性网络的动态网络嵌入。以及（3）通过利用链接权重和跨媒体链接对动态属性网络进行建模来开发特征选择方法，以进一步实现网络分析中的可解释性。此外，该项目将把研究问题纳入新的课程，也将使PI继续努力为本科生和代表性不足的学生提供研究机会。

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.