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Carlea Holl-Jensen||


Sharma, P., Khurana, U., Scharrenbroich, M., Locke, J. (June 2010)
Speeding up Network Layout and Centrality Measures with NodeXL and the Nvidia CUDA Technology

In this paper we talk about speeding up calculation of graph metrics and layout with NodeXL by exploiting the parallel architecture of modern day Graphics Processing Units (GPU), specifically Compute Unified Device Architecture (CUDA) by Nvidia. Graph centrality metrics like Eigenvector, Betweenness, Page Rank and layout algorithms like Fruchterman-Rheingold are essential components of Social Network Analysis (SNA). With the growth in adoption of SNA in different domains and increasing availability of huge networked datasets for analysis, social network analysts are looking for tools that are faster and more scalable. Our results show up to 802 times speedup for a Fruchterman-Rheingold graph layout and up to 17,972 times speedup for Eigenvector centrality metric calculations.

Q&A: Supporting Engagement and Peer Learning in a Classroom Setting Screenshot

Q&A: Supporting Engagement and Peer Learning in a Classroom Setting
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