P-WLPA algorithm research on parallel framework Spark

机译：P-WLPA算法并行框架火花研究

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Weighted Label Propagation Algorithm with Probability Threshold (P-WLPA) with typical serial execution prototype is proposed to be applied in data classification. Meanwhile on distributed computing system Spark, parallel P-WLPA algorithm for labeling big data is conducted. The algorithm sets prior conditions when configuring undirected graph and optimizes parameter learning in P-WLPA process. Through experiments, we analyze the relationship between Iterations for convergence, sample stability threshold and error rate. Finally Serial and parallel P-WLPA performance comparison demonstrates the feasibility and efficiency of the parallel P-WLPA algorithm implementation on Spark.

机译：提出了具有典型串行执行原型的概率阈值（P-WLPA）的加权标签传播算法以应用于数据分类。同时对分布式计算系统的火花，进行了用于标记大数据的并行P-WLPA算法。该算法在配置无向图表时设置先前的条件并在P-WLPA过程中优化参数学习。通过实验，我们分析了收敛性迭代之间的关系，采样稳定性阈值和误差率。最后串行和并行P-WLPA性能比较展示了Spark上并行P-WLPA算法实现的可行性和效率。

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《IEEE Joint International Information Technology and Artificial Intelligence Conference》|2014年||共5页
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Shuang Wang; Tao Liu; Peng Wu; Wei Min Kong;
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中图分类人工智能理论;
关键词
P-WLPA algorithm; Parallel computing; Spark; big data;

机译：P-WLPA算法;并行计算;火花;大数据;

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P-WLPA algorithm research on parallel framework Spark

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