CAREER: Novel Data Mining Technologies for Complex Network Analysis

A model complex network is a large system of elements (vertices) that are joined by non-trivial relationships (edges). Examples of such complex networks include the WWW, metabolic and protein networks, social networks, and economic and financial markets. The underlying principles and laws of these network systems can help us construct more effective communication mechanisms, find cures for fatal diseases, and deal with economic crises.

Building upon an innovative blend of graph theoretical, information theoretical, and statistical learning concepts and techniques, the proposed mining methodologies in this project will 1) extract network backbones which both simplify and highlight network structures, 2) measure the network difference for comparative network analysis, and 3) apply causal inference to integrate time series with network topology. In a close collaboration with domain experts from bioinformatics, political science, and software engineering, the proposed techniques have the potential to help reveal the organizational principles of biocellular systems in a dynamic environment; identify therapeutic or drug targets; illuminate how large scale software systems form and evolve; and understand how human society is organized at the individual level (social networks) and organizational level (political science). Using the popular online social networks, such as MySpace and Facebook, as "hooks", this project will attract, recruit, and prepare students from underrepresented groups including women and minorities to computer science and involve underrepresented students in the cutting-edge research.




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This material is based upon work supported by National Science Foundation under CAREER Award IIS-0953950. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.