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Distributed frequent subgraph mining on evolving graph using SPARK
Intelligent Data Analysis ( IF 1.7 ) Pub Date : 2020-05-21 , DOI: 10.3233/ida-194601
N. Senthilselvan 1 , V. Subramaniyaswamy 1 , V. Vijayakumar 2 , Hamid Reza Karimi 3 , N. Aswin 1 , Logesh Ravi 4
Affiliation  

Within the graph mining context, frequent subgraph identification plays a key role in retrieving required information or patterns from the huge amount of data in a short period. The problem of finding frequent items in traditional mining changed to the innovation of subgraphs that recurrently occurs in graph datasets containing a single huge graph. Majority of the existing methods target static graphs, and the distributed solution for dynamic graphs has not been explored. But, in modern applications like Facebook, robotics utilizes large evolving graphs. The goal is to design a method to find recurrent subgraphs from a single large evolving graph. In this research paper, a novel approach is proposed called DFSME, which uses SPARK to discover frequent subgraphs from an evolving graph in a distributed environment. DFSME maintains a set of subgraphs between frequent and infrequent subgraphs, which is used to decrease the search space. Our experiments with synthetic and real-world datasets authorize the effectiveness of DFSME for mining of recurrent subgraphs from huge evolving graph datasets.

中文翻译:

使用SPARK在演化图上进行分布式频繁子图挖掘

在图挖掘上下文中,频繁的子图识别在短期内从大量数据中检索所需的信息或模式中起着关键作用。在传统采矿中发现频繁项的问题变成了子图的创新,这种子图经常出现在包含单个巨大图的图数据集中。现有方法中的大多数是针对静态图的,尚未探索动态图的分布式解决方案。但是,在诸如Facebook之类的现代应用程序中,机器人技术利用了大型的进化图。目的是设计一种从单个大型演化图中查找循环子图的方法。在这篇研究论文中,提出了一种称为DFSME的新方法,该方法使用SPARK从分布式环境中的演化图中发现频繁的子图。DFSME在频繁和不频繁子图之间维护一组子图,用于减少搜索空间。我们对合成数据集和真实世界数据集的实验证明了DFSME在从庞大的演化图数据集中挖掘循环子图的有效性。
更新日期:2020-06-30
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