Deducing the Intermal Structure of Hidden Variables from Network Topology.

Abstract

This research is focused on the development of a model of geometric random graphs embedded into the hyperbolic plane, which captures the degree distribution and clustering behavior of many large real world networks. This model aims to build a genuine understanding of what the data is telling us about the biological, social, strategic, economic, and computational processes which underlay its generation.

Document Details

Document Type
DoD Grant Award
Publication Date
May 11, 2016
Source ID
N660011514064

Entities

People

  • David Rideout

Organizations

  • Defense Advanced Research Projects Agency
  • Naval Information Warfare Center Pacific
  • University of California, San Diego

Tags

Readers

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