Empirical Evaluation of Different Feature Representations for Social Circles Detection

Abstract

Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. We propose in this paper an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks. We compare our results with several different baselines.

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Document Details

Document Type
Technical Report
Publication Date
Jun 16, 2015
Accession Number
AD1001290

Entities

People

  • Jesús Alonso
  • Paolo Rosso
  • Roberto Paredes

Organizations

  • Technical University of Valencia

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Clustering
  • Communities
  • Computational Complexity
  • Detection
  • Friendship
  • Military Research
  • Models
  • Networks
  • Percolation
  • Social Media
  • Social Networking Services
  • Social Networks
  • Technology Transfer
  • Test And Evaluation
  • World Wide Web

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Graph Algorithms and Convex Optimization.