Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/MLSP.2012.6349745 | Machine Learning for Signal Processing |
Keywords | Field | DocType |
complex networks,graph theory,social networking (online),stochastic processes,Poisson based model,complex models,complex networks feature relations,edge-weight information,link prediction,social networks,weighted networks,Complex networks,Link-Prediction,Non-negative Matrix Factorization,Stochastic Blockmodels,weighted graphs | Graph theory,Computer science,Stochastic process,Complex network,Artificial intelligence,Non-negative matrix factorization,Stochastic geometry models of wireless networks,Machine learning,Network structure | Conference |
ISSN | ISBN | Citations |
1551-2541 E-ISBN : 978-1-4673-1025-3 | 978-1-4673-1025-3 | 1 |
PageRank | References | Authors |
0.36 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Kofoed Wind | 1 | 27 | 2.13 |
| Morten Mørup | 2 | 704 | 51.29 |