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

属性网络是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，这些消息可以表示为一系列单词属性；在卫生保健系统中，由于共享患者，提供者彼此联网，并且每个提供者都有个人资料信息，并且可以将保险索赔作为属性信息提交。特征学习的目的是在为各种数据挖掘任务准备属性网络时寻求数据实例的有效表示。特征学习算法，包括特征提取和特征选择，已经在文献中得到了深入的研究。虽然大多数现有研究都集中在静态、纯和浅网络上，但本项目旨在为动态属性网络开发新的特征学习算法。该项目的输出将是一系列特征学习算法，包括浅层和深层网络嵌入，以及专门为动态属性网络设计的特征选择。开发的算法及其相应的理论理解有望显著推进数据驱动的社会计算和卫生信息学。本项目的目标是开发一种新的动态属性网络特征学习框架，该框架由网络嵌入和相应的深度架构以及特征选择算法组成。该特征学习框架能够从各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进行动态网络嵌入，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)在不同类型的属性网络上设计动态网络嵌入的深度体系结构；(3)通过对带有链路权重和跨媒体链接的动态属性网络建模，开发特征选择方法，进一步提高网络分析的可解释性。此外，该项目将把研究问题纳入新课程，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.