Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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.
|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|
|State||Published - 2012|
|Conference||2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)|
|Period||23/10/2012 → 26/10/2012|
|Name||Machine Learning for Signal Processing|
|Citations||Web of Science® Times Cited: No match on DOI|
- Complex networks, Weighted graphs, Stochastic Blockmodels, Non-negative Matrix Factorization, Link-Prediction
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