Please use this identifier to cite or link to this item:
|Title:||Fast community detection|
|Source:||Song, Y.,Bressan, S. (2013). Fast community detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8055 LNCS (PART 1) : 404-418. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40285-2_35|
|Abstract:||We propose an algorithm for the detection of communities in networks. The algorithm exploits degree and clustering coefficient of vertices as these metrics characterize dense connections, which, we hypothesize, are indicative of communities. Each vertex, independently, seeks the community to which it belongs by visiting its neighbour vertices and choosing its peers on the basis of their degrees and clustering coefficients. The algorithm is intrinsically data parallel. We devise a version for Graphics Processing Unit (GPU). We empirically evaluate the performance of our method. We measure and compare its efficiency and effectiveness to several state of the art community detection algorithms. Effectiveness is quantified by five metrics, namely, modularity, conductance, internal density, cut ratio and weighted community clustering. Efficiency is measured by the running time. Clearly the opportunity to parallelize our algorithm yields an efficient solution to the community detection problem. © 2013 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 8, 2018
checked on Apr 20, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.