@CONFERENCE\{IMM2012-06507,
    author       = "D. K. Wind and M. M{\o}rup",
    title        = "Link prediction in weighted networks",
    year         = "2012",
    booktitle    = "Machine Learning for Signal Processing (MLSP), 2012 {IEEE} International Workshop on",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6507-full.html",
    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 \&\#64257;ve 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 \&\#64257;nd 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."
}