Associate Professor
Technical University of Denmark
In community detection, the theme of correctly identifying overlapping nodes, i.e. nodes which belong to more than one community, is important as it is related to role detection and to the improvement of the quality of clustering: proper detection of overlapping nodes gives a better understanding of the community structure. In this paper, we introduce a novel measure, called cuttability, that we show being useful for reliable detection of overlaps among communities and for improving the quality of the clustering, measured via modularity. The proposed algorithm shows better behaviour than existing techniques on the considered datasets (IRC logs and Enron e-mail log). The best behaviour is caught when a network is split between microcommunities. In that case, the algorithm manages to get a better description of the community structure.
In Proceedings of IEEE Symposium on Computational Intelligence and Data Mining (IEEE SSCI CIDM 2014); Orlando, Florida, USA; December 9-12, 2014.
Copyright and moral rights for the publications made accessible in the public website are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Latest website update: 21 September 2023.