Information Extraction from Large-Multi-Layer Social Networks

Abstract

Social networks often encode community structure using multiple distinct types of links between nodes. In this paper we introduce a novel method to extract information from such multi-layer networks, where each type of link forms its own layer. Using the concept of Pareto optimality, community detection in this multi-layer setting is formulated as a multiple criterion optimization problem. We propose an algorithm for finding an approximate Pareto frontier containing a family of solutions. The power of this approach is demonstrated on a Twitter dataset, where the nodes are hashtags and the layers correspond to (1) behavioral edges connecting pairs of hashtags whose temporal profiles are similar and (2) relational edges connecting pairs of hashtags that appear in the same tweets.

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Document Details

Document Type
Technical Report
Publication Date
Aug 06, 2015
Accession Number
AD1023792

Entities

People

  • Alex Kulesza
  • Alfred Hero
  • Brandon Oselio

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Communities
  • Computer Science
  • Detection
  • Electrical Engineering
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Multiobjective Optimization
  • Online Communications
  • Optimization
  • Phase Transformations
  • Signal Processing
  • Social Media
  • Social Networking Services
  • Social Networks

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Computer Networking
  • Computer Programming and Software Development.

Technology Areas

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