Link prediction in weighted networks

AbstractMany 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.
TypeConference paper [With referee]
ConferenceMachine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing