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

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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.