Link prediction in weighted networks

David Kofoed Wind, Morten Mørup

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Many complex networks feature relations with weight information. Some models utilize this information while other ignore the weight information when inferring the structure. In this paper we investigate if edge-weights when modeling real networks, carry important information about the network structure. We compare five prominent models by their ability to predict links both in the presence and absence of weight information. In addition we quantify the models ability to account for the edge-weight information. We find that the complex models generally outperform simpler models when the task is to infer presence of edges, but that simpler models are better at inferring the actual weights.
Original languageEnglish
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
Place of Publication978-1-4673-1025-3
PublisherIEEE
Publication date2012
ISBN (Print)978-1-4673-1024-6
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain
Duration: 23 Oct 201226 Oct 2012
http://mlsp2012.conwiz.dk/

Conference

Conference2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
CountrySpain
CitySantander
Period23/10/201226/10/2012
Internet address
SeriesMachine Learning for Signal Processing
ISSN1551-2541

Keywords

  • Complex networks
  • Weighted graphs
  • Stochastic Blockmodels
  • Non-negative Matrix Factorization
  • Link-Prediction

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