Multi-Attribute Networks and the Impact of Partial Information on Inference and Characterization

• Activity Number: 310
• Type: Invited
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: General Methodology
• Author(s): Natallia V Katenka*+
• Companies: Boston University
• Keywords: {multi-attribute association networks ; gene/protein regulatory networks ; canonical correlation

Abstract

Our work is motivated by application in the context of gene/protein regulatory networks in human cancer cells. Association networks represent systems of interacting elements, where a link between two different elements indicates a sufficient level of similarity between element attributes. While in reality relational ties between elements can be based on similarity across multiple attributes, the majority of work to date on association networks involves ties defined on a single attribute. Our approach for the inference of multi-attribute association networks from measurements on continuous attribute variables is to use canonical correlation and a hypothesis-testing strategy. We study the impact of partial information on multi-attribute network inference and characterization, when only a subset of attributes is available. We examine the implications of the choice and number of node attributes on the ability to detect network links and, more generally, to estimate higher-level network such as node degree, clustering coefficients, and measures of centrality.