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

Software Engineering Institute

East China Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1388-1394
Number of pages	7
ISBN (Electronic)	9781538662434
DOIs	https://doi.org/10.1109/ITNEC.2019.8729187
State	Published - Mar 2019
Event	3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 - Chengdu, China Duration: 15 Mar 2019 → 17 Mar 2019

Publication series

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

Conference

Conference	3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
Country/Territory	China
City	Chengdu
Period	15/03/19 → 17/03/19

Keywords

Decision tree
Graph partition
KNN
Metrics

Access to Document

10.1109/ITNEC.2019.8729187

Cite this

Yin, Z., & Cao, Z. (2019). Metrics for graph partition by using machine learning techniques. In B. Xu (Ed.), Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 (pp. 1388-1394). Article 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. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 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. in B Xu (ed.), 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., pp. 1388-1394, 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019, Chengdu, China, 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. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1388-1394 8729187 (Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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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. In Xu B, editor, Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 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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