Link prediction in weighted networks

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

View graph of relations

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
Title2012 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
StatePublished

Conference

Conference2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
CountrySpain
CitySantander
Period23/10/1226/10/12
Internet addresshttp://mlsp2012.conwiz.dk/
NameMachine Learning for Signal Processing
ISSN (Print)1551-2541
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Complex networks, Weighted graphs, Stochastic Blockmodels, Non-negative Matrix Factorization, Link-Prediction
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

ID: 51176437