Efficient Structural Clustering in Large Uncertain Graphs

Clustering uncertain graphs based on the probabilistic graph model has sparked extensive research and widely varying applications. Existing structural clustering methods rely heavily on the computation of pairwise reliable structural similarity between vertices, which has proven to be extremely cost...

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Bibliographic Details
Published in2020 IEEE 36th International Conference on Data Engineering (ICDE) pp. 1966 - 1969
Main Authors Liang, Yongjiang, Hu, Tingting, Zhao, Peixiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2020
Subjects
Computer science
Indexes
Probabilistic logic
Reliability theory
Structural Clustering
Time complexity
Uncertain Graphs
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Summary:Clustering uncertain graphs based on the probabilistic graph model has sparked extensive research and widely varying applications. Existing structural clustering methods rely heavily on the computation of pairwise reliable structural similarity between vertices, which has proven to be extremely costly, especially in large uncertain graphs. In this paper, we develop a new, decomposition-based method, ProbSCAN, for efficient reliable structural similarity computation with theoretically improved complexity. We further design a cost-effective index structure UCNO-Index, and a series of powerful pruning strategies to expedite reliable structural similarity computation in uncertain graphs. Experimental studies on eight real-world uncertain graphs demonstrate the effectiveness of our proposed solutions, which achieves orders of magnitude improvement of clustering efficiency, compared with the state-of-the-art structural clustering methods in large uncertain graphs.
ISSN:2375-026X
DOI:10.1109/ICDE48307.2020.00215