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EvoGraph: An effective and efficient graph upscaling method for preserving graph properties

Title
EvoGraph: An effective and efficient graph upscaling method for preserving graph properties
Authors
Park, Him ChanKim, Min-Soo
DGIST Authors
Kim, Min-Soo
Issue Date
2018-08-19
Citation
ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2051-2059
Type
Conference
ISBN
9781450355520
Abstract
Nowadays, many researchers and industry groups often suffer from the lack of a variety of large-scale real graphs. Although a lot of synthetic graph generation methods (or models) such as RMAT and BA have been developed, their output graphs tend to be quite different from real-world graphs in terms of graph properties. There are a few graph upscaling methods such as Gscaler, they still fail to preserve important properties of the original graph and fail to upscale due to out of memory or too long runtime. In this paper, we propose a novel graph upscaling method called EvoGraph that can upscale the original graph with preserving its properties regardless of a scale factor. It determines and attaches new edges to the real graph using the preferential attachment mechanism in an effective and efficient way. Through extensive experiments, we have demonstrated that EvoGraph significantly outperforms the state-of-the-art graph upscaling method Gscaler in terms of preserving graph properties and performance measures such as runtime, memory usage, and scalability. © 2018 Association for Computing Machinery.
URI
http://hdl.handle.net/20.500.11750/9266
DOI
10.1145/3219819.3220123
Publisher
Association for Computing Machinery
Files:
There are no files associated with this item.
Collection:
Department of Information and Communication EngineeringInfoLab2. Conference Papers


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