Graph sampling techniques for precise estimation of large-scale graph measures
| Project/Area Number |
26540161
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Web informatics, Service informatics
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Shudo Kazuyuki 東京工業大学, 情報理工学(系)研究科, 准教授 (90308271)
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| Co-Investigator(Kenkyū-buntansha) |
AKIOKA SAYAKA 明治大学, 総合数理学部, 准教授 (90333533)
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2014: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | グラフサンプリング / 大規模グラフ |
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| Outline of Final Research Achievements |
Graph sampling is an effective approach to estimate measures of graphs in case it is not possible to analyze an etire graph for reasons such as difficulty in obtaining and its great magnitude. We took two approaches to improve precision of the estimation. An approach is assuming a target graph to be a complex network and utilizing the assumption. Another approach is replacing normal random walk with non-backtracking random walk. The latter approach reduced the number of sampling steps to collect a certain number of vertexes in comparison to the existing best technique and improved the precision even with the same number of sampled vertexes.
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Report
(3 results)
Research Products
(2 results)
