Metrics for graph partition by using machine learning techniques

Zhuochao Yin; Zhenfu Cao

doi:10.1109/ITNEC.2019.8729187

Metrics for graph partition by using machine learning techniques

Zhuochao Yin
, Zhenfu Cao

软件工程学院

East China Normal University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

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.

源语言	英语
主期刊名	Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
编辑	Bing Xu
出版商	Institute of Electrical and Electronics Engineers Inc.
页	1388-1394
页数	7
ISBN（电子版）	9781538662434
DOI	https://doi.org/10.1109/ITNEC.2019.8729187
出版状态	已出版 - 3月 2019
活动	3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 - Chengdu, 中国期限: 15 3月 2019 → 17 3月 2019

出版系列

姓名	Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019

会议

会议	3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
国家/地区	中国
市	Chengdu
时期	15/03/19 → 17/03/19

访问文件

10.1109/ITNEC.2019.8729187

其它文件与链接

链接到 Scopus 的出版物

引用此

Yin, Z., & Cao, Z. (2019). Metrics for graph partition by using machine learning techniques. 在 B. Xu (编辑), Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 (页码 1388-1394). 文章 8729187 (Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNEC.2019.8729187

Yin, Zhuochao ; Cao, Zhenfu. / Metrics for graph partition by using machine learning techniques. Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019. 编辑 / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 1388-1394 (Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019).

@inproceedings{d50fede46e8b4ad784073e785924ebbb,

title = "Metrics for graph partition by using machine learning techniques",

abstract = "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.",

keywords = "Decision tree, Graph partition, KNN, Metrics",

author = "Zhuochao Yin and Zhenfu Cao",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 ; Conference date: 15-03-2019 Through 17-03-2019",

year = "2019",

month = mar,

doi = "10.1109/ITNEC.2019.8729187",

language = "英语",

series = "Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1388--1394",

editor = "Bing Xu",

booktitle = "Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019",

address = "美国",

}

Yin, Z & Cao, Z 2019, Metrics for graph partition by using machine learning techniques. 在 B Xu (编辑), Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019., 8729187, Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019, Institute of Electrical and Electronics Engineers Inc., 页码 1388-1394, 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019, Chengdu, 中国, 15/03/19. https://doi.org/10.1109/ITNEC.2019.8729187

Metrics for graph partition by using machine learning techniques. / Yin, Zhuochao; Cao, Zhenfu.
Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019. 编辑 / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 1388-1394 8729187 (Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Metrics for graph partition by using machine learning techniques

AU - Yin, Zhuochao

AU - Cao, Zhenfu

PY - 2019/3

Y1 - 2019/3

N2 - 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.

AB - 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.

KW - Decision tree

KW - Graph partition

KW - KNN

KW - Metrics

UR - https://www.scopus.com/pages/publications/85067886715

U2 - 10.1109/ITNEC.2019.8729187

DO - 10.1109/ITNEC.2019.8729187

M3 - 会议稿件

AN - SCOPUS:85067886715

T3 - Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019

SP - 1388

EP - 1394

BT - Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019

Y2 - 15 March 2019 through 17 March 2019

ER -

Yin Z, Cao Z. Metrics for graph partition by using machine learning techniques. 在 Xu B, 编辑, Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 页码 1388-1394. 8729187. (Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019). doi: 10.1109/ITNEC.2019.8729187

Metrics for graph partition by using machine learning techniques

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