Fast Community Detection
| dc.contributor.author | SONG, Yi | en_US |
| dc.contributor.author | BRESSAN, Stephane | en_US |
| dc.date.accessioned | 2013-05-21T01:36:27Z | en_US |
| dc.date.accessioned | 2017-01-23T07:00:07Z | |
| dc.date.available | 2013-05-21T01:36:27Z | en_US |
| dc.date.available | 2017-01-23T07:00:07Z | |
| dc.date.issued | 2013-05-21T01:36:27Z | en_US |
| dc.description.abstract | We propose an algorithm for detecting communities in networks. The algorithm exploits degree and clustering coefficient of vertices as we hypothesize that these metrics characterize dense connections indicative of a community. Each vertex, independently, seeks the community to which it belongs by visiting its neighbor 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. | en_US |
| dc.format.extent | 572638 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/4145 | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | ;TRC5/13 | en_US |
| dc.title | Fast Community Detection | en_US |
| dc.type | Technical Report | en_US |