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http://dx.doi.org/10.3837/tiis.2019.06.012

Mining Highly Reliable Dense Subgraphs from Uncertain Graphs  

LU, Yihong (College of Computer Science & Technology, Zhejiang University of Technology)
HUANG, Ruizhi (College of Computer Science & Technology, Zhejiang University of Technology)
HUANG, Decai (College of Computer Science & Technology, Zhejiang University of Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.6, 2019 , pp. 2986-2999 More about this Journal
Abstract
The uncertainties of the uncertain graph make the traditional definition and algorithms on mining dense graph for certain graph not applicable. The subgraph obtained by maximizing expected density from an uncertain graph always has many low edge-probability data, which makes it low reliable and low expected edge density. Based on the concept of ${\beta}$-subgraph, to overcome the low reliability of the densest subgraph, the concept of optimal ${\beta}$-subgraph is proposed. An efficient greedy algorithm is also developed to find the optimal ${\beta}$-subgraph. Simulation experiments of multiple sets of datasets show that the average edge-possibility of optimal ${\beta}$-subgraph is improved by nearly 40%, and the expected edge density reaches 0.9 on average. The parameter ${\beta}$ is scalable and applicable to multiple scenarios.
Keywords
uncertain graph; network reliability; surplus average degree; optimal ${\beta}$-subgraph; graph mining;
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