On October 07, 2014, at 14:00, in the library of the Institute of Physics Belgrade, Darko Hric (Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Finland)  presents a seminar talk entitled:
Detecting communities in networks is one of the most popular topics of network science. Communities, or clusters, are usually conceived as subgraphs of a network, with a high density of links within the subgraphs and a comparatively lower density between them. Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the annotated clusters in many large networks. Our results show that there is a marked separation between structural and annotated clusters, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that annotated clusters may not be recoverable from topology alone.