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A Review of Big Graph Mining Research

机译:大图矿业研究综述

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摘要

Big Graph Mining" is a continuously developing research that was started in 2009 until now. After 7 years, there are many researches that put this topic as the main concern. However, there is no mapping or summary concerning the important issues and solutions to explain this topic. This paper contains a summary of researches that have been conducted since 2009. The result is grouped based on the algorithms, built system and also preprocess techniques that have been developed. Based on survey, there are 11 algorithms and 6 distributed systems to analyse the Big Graph have been improved. While improved pre-process algorithm only covers: sampling and compression technique. These improving algorithms are usually aimed to frequent sub graphs discovery, whereas slightly those of is aimed to cluster Big Graph, and there is no algorithm to classify Big Graph. As a conclusion of this survey, there is a need for more researches to be conducted to improve a comprehensive Graph Mining System, especially for very big Graph.
机译:Big Graph Mining“是一项持续开发的研究,直到现在2009年。7年后,有许多研究将这一主题作为主要关注点。但是,没有关于解释的重要问题和解决方案的映射或摘要这个主题。本文包含自2009年以来已经进行的研究摘要。结果基于算法,​​内置系统和已开发的预处理技术进行分组。基于调查,有11个算法和6个分布式系统分析大图已经提高了。虽然改进的预流程算法仅涵盖:采样和压缩技术。这些改进的算法通常旨在经常频繁的子图,而略微瞄准大图,并且没有算法分类大图。作为本调查的结论,需要进行更多的研究,以改善全面的图形挖掘系统,ESPE对于非常大的图表。

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