Abstract
Community discovery has often been achieved by assigning each node in a network only to one community. However, a node (e.g., user) might belong to several communities in real world networks. For undirected connected networks without self-loops, we proposed weighted line graphs based on the weights of the original network, as they do not contain self-loops as in the standard line graph in general graph theory. Overlapping community discovery is achieved by applying some off-the-shelf node partitioning method to the weighted line graphs. In this paper we report a performance evaluation of the weighted line graphs over both synthetic and real-world networks. The effectiveness of the weighted line graphs are investigated in terms of both the visualization of discovered communities and the generalized modularity measure. The results show that both the utilization of weights in the original networks and the self-loop free property contribute to the performance improvement.
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Yoshida, T. (2013). Weighted Line Graphs for Overlapping Community Discovery and their Evaluation. In: Tanaka, Y., Spyratos, N., Yoshida, T., Meghini, C. (eds) Information Search, Integration and Personalization. ISIP 2012. Communications in Computer and Information Science, vol 146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40140-4_13
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DOI: https://doi.org/10.1007/978-3-642-40140-4_13
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