Link prediction in weighted networks

David Kofoed Wind; Morten Mørup

doi:10.1109/MLSP.2012.6349745

Link prediction in weighted networks

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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 language	English
Title of host publication	2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages	6
Place of Publication	978-1-4673-1025-3
Publisher	IEEE
Publication date	2012
ISBN (Print)	978-1-4673-1024-6
DOIs	https://doi.org/10.1109/MLSP.2012.6349745
Publication status	Published - 2012
Event	2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain Duration: 23 Oct 2012 → 26 Oct 2012 http://mlsp2012.conwiz.dk/

Conference

Conference	2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Country	Spain
City	Santander
Period	23/10/2012 → 26/10/2012
Internet address	http://mlsp2012.conwiz.dk/

Series	Machine Learning for Signal Processing
ISSN	1551-2541

Keywords

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

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10.1109/MLSP.2012.6349745

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Cite this

@inproceedings{9693cc26961b46c39c269ac4b7118416,

title = "Link prediction in weighted networks",

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.",

keywords = "Complex networks, Weighted graphs, Stochastic Blockmodels, Non-negative Matrix Factorization, Link-Prediction",

author = "Wind, {David Kofoed} and Morten M{\o}rup",

year = "2012",

doi = "10.1109/MLSP.2012.6349745",

language = "English",

isbn = "978-1-4673-1024-6",

series = "Machine Learning for Signal Processing",

booktitle = "2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)",

publisher = "IEEE",

address = "United States",

note = "2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) ; Conference date: 23-10-2012 Through 26-10-2012",

url = "http://mlsp2012.conwiz.dk/",

}

Wind, DK & Mørup, M 2012, Link prediction in weighted networks. in 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 978-1-4673-1025-3, Machine Learning for Signal Processing, 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Santander, Spain, 23/10/2012. https://doi.org/10.1109/MLSP.2012.6349745

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T1 - Link prediction in weighted networks

AU - Wind, David Kofoed

AU - Mørup, Morten

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Complex networks

KW - Weighted graphs

KW - Stochastic Blockmodels

KW - Non-negative Matrix Factorization

KW - Link-Prediction

U2 - 10.1109/MLSP.2012.6349745

DO - 10.1109/MLSP.2012.6349745

M3 - Article in proceedings

SN - 978-1-4673-1024-6

T3 - Machine Learning for Signal Processing

BT - 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

PB - IEEE

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T2 - 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

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