Optimization Techniques for Analysis of Biological and Social Networks

Abstract

This project focused on a multifaceted study of a class of cluster-detection problems arising in biological and social networks. This includes defining new cluster models and their alternative mathematical programming formulations, their theoretical analysis, the development of exact algorithms, and heuristics. Originally, clusters (complexes, modules, cohesive subgroups) in biological and social networks were described by cliques (complete subgraphs) or connected components. However, in many practical situations cliques appear to be overly restrictive, whereas connected components are insufficiently "tight" clusters. This project considers a class of concepts describing clusters that "relax" the definition of a clique and are tighter than connected components. Such problems are of great practical as well as theoretical interest and this project is the first attempt to approach the clique relaxation models in a systematic fashion under a unifying theoretical and algorithmic framework.

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Document Details

Document Type
Technical Report
Publication Date
Mar 28, 2012
Accession Number
ADA567067

Entities

People

  • Sergiy Butenko

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Communication Systems
  • Computational Biology
  • Computational Complexity
  • Computer Programming
  • Computer Science
  • Contracts
  • Data Mining
  • Emerging Technology
  • Engineering
  • Industrial Engineering
  • Integer Programming
  • Mathematical Programming
  • Optimization
  • Social Networks
  • Systems Engineering
  • Universities

Readers

  • Graph Algorithms and Convex Optimization.
  • Neural Network Machine Learning.
  • Systems Analysis and Design