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Pregel Slides, Page Rank Slides, Pregel: A System for Large Scale Graph Processing, Scaling!
Graph500 is a data intensive super computing application which uses a large scale graph to evaluate the performance.
To support large scale graph databases, existing research work has mainly investigated the data organization and computational models.
Large-scale graphs have become ubiquitous in social media.
In recent years, many distributed graph-processing systems have been designed and developed to analyze large-scale graphs.
As testbed, we use synthetically generated large-scale graphs carefully chosen to cover a wide range of input types.
We demonstrate the efficiency of our algorithm, comparing its performance on several "benchmark" large-scale graphs with existing methods.
Data scientists are faced with the challenge of analyzing large-scale graphs that are changing dynamically, while existing tools and metaphors for data collection, processing, storage, and analysis are not suitable for handling large-scale evolutionary graphs.
A number of algorithms to subdivide the nodes of large-scale graphs into communities have recently been proposed; many of them hunt for the graph's partitions of maximal modularity.
By using three typical large-scale graphs and three popular graph-processing algorithms, we conduct comprehensive experiments to study the performance of our and of many alternative streaming policies on a real distributed graph-processing system.
Basically, they are large-scale graphs with nontrivial connection patterns [1].
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