Conference paper
Link prediction in weighted networks
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Department of Informatics and Mathematical Modeling, Technical University of Denmark3
Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark4
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2012 |
| Pages: | 1-6 |
| Proceedings: | 2012 IEEE International Workshop on Machine Learning for Signal Processing |
| Series: | Machine Learning for Signal Processing |
| ISBN: | 1467310247 , 1467310255 , 1467310263 , 9781467310246 , 9781467310253 and 9781467310260 |
| ISSN: | 21610363 and 15512541 |
| Types: | Conference paper |
| DOI: | 10.1109/MLSP.2012.6349745 |
| ORCIDs: | Mørup, Morten |
Complex networks Link-Prediction Non-negative Matrix Factorization Stochastic Blockmodels Weighted graphs
Analytical models Clustering algorithms Complexity theory Image edge detection Poisson based model Predictive models Stochastic processes complex models complex networks complex networks feature relations edge-weight information graph theory link prediction social networking (online) social networks stochastic processes weighted graphs weighted networks
