CAREER: Novel Data Mining Technologies for Complex Network Analysis

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The long-term research goal is to develop novel data miningtechnologies to elucidate the structures and dynamics of complexbut ubiquitous networks. A complex network is a large systemof 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 economicand financial markets. The underlying principles and laws of thesenetwork systems can help us construct more effectivecommunication mechanisms, find cures for fatal diseases, anddeal with economic crises.In spite of the significant advances that have been made towardsunderstanding the fundamental laws that govern the structure andbehavior of complex networks, there is still a disconnect between current analytical techniques and their applicability to real-worldcomplex networks. A principled approach is lacking to systematicallyanalyze a single large complex network and link system behaviors to network structure. There is also immediate and crucial need for atheoretical framework to understand the relationships betweenmultiple networks, which is the key for comparative network analysis. How to integrate and leverage the rich system data, such asmeasurement time series, associated with network topology to studycomplex systems is still an open question. This project addressesthese issues by: 1) developing novel graph and informationtheoretical approaches to extracting network backbones which bothsimplify and highlight network structures; 2) developing informationtheoretical network distance measures and clustering algorithms forcomparative network analysis; 3) applying causality inference andnetwork modularity to integrate time series with network topology.The proposed mining methodologies build upon an innovative blendof graph theoretical, information theoretical, and statistical learningconcepts and techniques, and can greatly expand the reach ofdata mining. This project will also help us better analyze theemerging complexity, heterogeneity, and large scale of real-worldcomplex network data.In a close collaboration with domain experts from social and politicalsciences, software engineering, and bioinformatics, the proposedtechniques have the potential to help understand how human society isorganized at the individual level (social networks) and organizationallevel (political science); illuminate how large scale software systemsform and evolve; reveal the organizational principles of biocellularsystems in a dynamic environment; and identify therapeutic or drugtargets. Using the popular online social networks, such as MySpaceand Facebook, as ``hooks'', this project will attract, recruit, andprepare students from underrepresented groups including womenand minorities to computer science and involve underrepresentedstudents in the cutting-edge research.For further information see the project web page at:http://www.cs.kent.edu/~jin/NSFCAREER/