Efficient Algorithmic Frameworks via Structural Graph Theory

Abstract

In this project, we developed many new efficient algorithms for analysis of networks. We have published over 100 papers during the course of this project, and we launched a new website BigDND [http://projects.csail.mit.edu/dnd/] for distributing large network data and tools for analyzing them. Within network science, our research develops algorithms to enable efficient and guaranteed-quality analysis of abroad range of types of networks, from social networks to computer networks and transportation networks. Real-world social networks of interest include online services (Facebook, Google , Twitter), coauthorship/collaboration among people (arXiv, DBLP, patents), phone calls (AT\ and T, NSA), in-person interactions (FBI, Pentagon), geographic hierarchical neighborhoods (living or working together, on the same block, in the same district or city), and shared interests (Netflix, Amazon, Match.com).

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Document Details

Document Type
Technical Report
Publication Date
Oct 28, 2016
Accession Number
AD1023352

Entities

People

  • Erik D. Demaine
  • Mohammad T. Hajiaghayi

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Computer Networks
  • Computer Science
  • Data Sets
  • Disaster Management
  • Electronic Mail
  • Graph Theory
  • Network Science
  • Network Topology
  • Social Media
  • Social Networking Services
  • Social Networks
  • Teamwork
  • Theoretical Computer Science
  • United States
  • Zero-Sum Games

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Government and Public Administration Law.