Graph Pattern Mining and Learning through User-Defined Relations

In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation. More specifically, we enable the computation of statistics of patterns through their subgraph classes, generalizing traditional GPM methods. R-GPM provides efficient...

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Bibliographic Details
Main Authors: Teixeira, Carlos H. C., Cotta, Leornado, Ribeiro, Bruno, Meira, Wagner
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Computer science
Data mining
Distance measurement
Graph mining
Neural networks
Optimization
random walk
sampling
subgraph pattern
Task analysis
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Description
Summary:In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation. More specifically, we enable the computation of statistics of patterns through their subgraph classes, generalizing traditional GPM methods. R-GPM provides efficient estimators for these statistics by employing a MCMC sampling algorithm combined with several optimizations. We provide both theoretical guarantees and empirical evaluations of our estimators in application scenarios such as stochastic optimization of deep high-order graph neural network models and pattern (motif) counting. We also propose and evaluate optimizations that enable improvements of our estimators accuracy, while reducing their computational costs in up to 3-orders-of-magnitude. Finally, we show that R-GPM is scalable, providing near-linear speedups.
ISSN:2374-8486
DOI:10.1109/ICDM.2018.00170