  We investigate the possibility of global optimization-based overlapping
community detection, using link community framework. We first show that
partition density, the original quality function used in link community
detection method, is not suitable as a quality function for global optimization
because it prefers breaking communities into triangles except in highly limited
conditions. We analytically derive those conditions and confirm it with
computational results on direct optimization of various synthetic and
real-world networks. To overcome this limitation, we propose alternative
approaches combining the weighted line graph transformation and existing
quality functions for node-based communities. We suggest a new line graph
weighting scheme, a normalized Jaccard index. Computational results show that
community detection using the weighted line graphs generated with the
normalized Jaccard index leads to a more accurate community structure.

Physics and Society;Social and Information Networks;Computational Physics