Tang receives NSF Grant

Jiliang Tang, Assistant Professor of Computer Science and Engineering at Michigan State University, has been awarded an NSF grant entitled "A General Feature Learning Framework for Dynamic Attributed Networks".

Different from traditional pure networks, many real-world information systems, which often associate with a rich set of attributes, are known as attributed networks. For example, in online social networks, users post messages related to what they are experiencing that 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 vector 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 literature. While most existing studies focused on static, pure and shallow networks, this project aims to develop novel feature learning algorithms to handle dynamic attributed networks. The output of the project is 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.

(Date Posted: 2017-07-28)