… we show that modularity optimization indeed does not resolve a large number of modules. A
… here key elements for the assessment of the reliability of this community detection method. …

… Resolution parameters improve things, but they do not (… community working on the problem
of community detection will address this issue in the future and that general structural limits of …

… For these reasons, we believe that a serious assessment of the goodness of community
detection algorithms can be made by evaluating their performance on the LFR benchmark. In …

… of a network into communities. Starting from … resolution limit that poses severe limits to its
applicability, we discuss the general issue of the use of quality functions in community detection…

… Community detection in networks, also called graph or network clustering, is an ill-defined …
This work presents a critical analysis of the problem of community detection, intended to …

… In Section 3 we define the basic elements of community detection, ie the concepts of
community and partition. Traditional clustering methods in computer and social sciences, ie graph …

… The aim of community detection in graphs is to identify the modules only based on the
topology. The problem has a long tradition and it has appeared in various forms in several …

… of Fortunato and Barth… community detection seems to have shifted away from modularity
maximization in favor of machine learning techniques. In this paper, we revisit the resolution limit …

… community detection methods currently in use and show its superior sensitivity in the detection
of … Finally, we discuss the implications of these findings for the identification of connector …

… neity in the distributions of node degrees and of community sizes. We use this benchmark
to test two popular methods of community detection, modularity optimization, and Potts model …