In this paper, we present graphics processing unit
(GPU) based implementations of three popular
shortest-path centrality metrics- closeness, eccentricity
and betweenness. The basic method is designed to
compute the centrality on gene-expression networks,
where the network is pre-constructed in the form of
kNN graphs from DNA microarray data sets. The relationship
among the genes in the kNN graph is determined
by the similarity of their expression levels. The
proposed method has been applied to a well known
breast cancer microarray study and we highlighted
the correlation of the highly ranked genes to the time
to relapse of the disease. The method is readily applicable
to other datasets, where the data points can
be recognised in a multidimensional space. It can be
applied to other networks (e.g., social networks, the
Internet, etc.) with minimal modi cations. |
| Cite as: Arefin, A.S., Berretta, R. and Moscato, P. (2015). On Ranking Nodes using kNN Graphs, Shortest-paths and GPUs. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 29-38 |
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