Metrics for Graph Partition by Using Machine Learning Techniques

被引：0

作者：

Yin, Zhuochao ^{[1
]}

Cao, Zhenfu ^{[1
]}

机构：

[1] East China Normal Univ, SEI, Shanghai, Peoples R China

来源：

PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019) | 2019年

关键词：

metrics; graph partition; decision tree; KNN;

D O I：

10.1109/itnec.2019.8729187

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In our previous work, we explored the possibility of applying machine learning technique to graph partition. We use some metrics to describe the graph, rank the execution time of some graph algorithm and feed them into the machine learning models. We proved that decision tree and KNN and good models of this problem. In the paper, we go on to investigate more metrics to describe the graph after partitioning. We found that AverageDegreeNotCut is also an important metric. We improve the precision score of original machine learning models by 4.9 percent.

引用

页码：1388 / 1394

页数：7

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