Dynamic Information Networks: Geometry, Topology and Statistical Learning for the Articulation of Structure

Abstract

The research objectives of this project were to create new mathematical tools for understanding different kinds of information networks, especially the dynamics thereof and also to import tools from geometry to analyze network dynamics. In particular, we aimed to create new mathematical frameworks for visualizing and teasing apart multiscale network dynamics. We see this as extremely relevant for the analysis of large document corpora. The primary technical approach exploits ideas from linear algebra, markov processes, diffusion networks, differential geometry, and machine learning.

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Document Details

Document Type
Technical Report
Publication Date
Jun 23, 2015
Accession Number
ADA624183

Entities

People

  • Daniel Rockmore

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Classification
  • Computer Science
  • Curvature
  • Data Mining
  • Differential Geometry
  • Diffusion
  • Dynamics
  • Electronic Mail
  • Geometry
  • Law
  • Learning
  • Linear Algebra
  • Machine Learning
  • Markov Processes
  • Mathematics
  • Network Science

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms