Evolve: Analyzing Evolving Social Networks

Abstract

Many current social network analytic methods work by analyzing a static aggregate graph, which provides a limited view of the structure and behavior of real-world social networks. Social networks in reality are dynamic and evolve over time as people join or leave the networks and new connections form. This work investigates developing dynamic social network analysis (DSNA) methods to explicitly model time and heterogeneity. It focuses on two objectives: (1) Dynamic SNA metrics and methods which take time into account; (2) Predictive methods for modeling and predicting how individuals and groups change over time.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2012
Accession Number
ADA564174

Entities

People

  • Sofus Macskassy

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • California
  • Data Mining
  • Data Sets
  • Government Procurement
  • Information Exchange
  • Information Science
  • Machine Learning
  • Media
  • Online Communications
  • Predictive Modeling
  • Social Media
  • Social Networking Services
  • Social Networks
  • Standards
  • United States

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development