Structural Change and Interaction Behavior in Multimodal Networks

Abstract

This work presents the results of research focused on mining information from multi-network interactions for the purpose of link prediction. Multi-networks are a generalization of multimodal networks. Multi-network link prediction was evaluated on the HEP-th (theoretical high-energy physics) authorship multinetwork. Achievements include 1) a novel iterative procedure for estimating unified multinetwork node similarity based only on the network structure information; 2) label propagation algorithm to perform adjacency propagation through the similarity matrices to produce a ranking of potential new links. The work also researched modelling engagingness and responsiveness behaviors in email networks and messaging networks. Several quantitative models for measuring user engagingness and responsiveness behaviors were defined.

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Document Details

Document Type
Technical Report
Publication Date
Jul 30, 2010
Accession Number
ADA525154

Entities

People

  • Belinda Wei-shan Toh
  • Byung-won On
  • Ee-peng Lim
  • Jing Jiang
  • Loo-nin Teow
  • Wen-haw Chong
  • Xinghao Pan

Organizations

  • Defence Science Organisation

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Data Sets
  • Databases
  • Electronic Mail
  • Information Science
  • Linear Accelerators
  • Machine Learning
  • Mobile Phones
  • Network Science
  • Particle Physics
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Random Walk
  • Social Networks
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.