GraphMiner: A structural pattern-mining system for large disk-based graph databases and its applications

Wei Wang, Chen Wang, Yongtai Zhu, Baile Shi, Jian Pei, Xifeng Yan, Jiawei Han

Research output: Contribution to journalConference article

Abstract

Mining frequent structural patterns from graph databases is an important research problem with broad applications. Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques. The techniques have been integrated into a research prototype system - GraphMiner. In this paper, we describe a demo of GraphMiner which showcases the technical details of the index structure and the mining algorithms including their efficient implementation, the mining performance and the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications.

Original languageEnglish (US)
Pages (from-to)879-881
Number of pages3
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
StatePublished - Dec 1 2005
EventSIGMOD 2005: ACM SIGMOD International Conference on Management of Data - Baltimore, MD, United States
Duration: Jun 14 2005Jun 16 2005

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

GraphMiner : A structural pattern-mining system for large disk-based graph databases and its applications. / Wang, Wei; Wang, Chen; Zhu, Yongtai; Shi, Baile; Pei, Jian; Yan, Xifeng; Han, Jiawei.

In: Proceedings of the ACM SIGMOD International Conference on Management of Data, 01.12.2005, p. 879-881.

Research output: Contribution to journalConference article

@article{cdeea12ac25447cc920acc8a3f30ee42,
title = "GraphMiner: A structural pattern-mining system for large disk-based graph databases and its applications",
abstract = "Mining frequent structural patterns from graph databases is an important research problem with broad applications. Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques. The techniques have been integrated into a research prototype system - GraphMiner. In this paper, we describe a demo of GraphMiner which showcases the technical details of the index structure and the mining algorithms including their efficient implementation, the mining performance and the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications.",
author = "Wei Wang and Chen Wang and Yongtai Zhu and Baile Shi and Jian Pei and Xifeng Yan and Jiawei Han",
year = "2005",
month = "12",
day = "1",
language = "English (US)",
pages = "879--881",
journal = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
issn = "0730-8078",
publisher = "Association for Computing Machinery (ACM)",

}

TY - JOUR

T1 - GraphMiner

T2 - A structural pattern-mining system for large disk-based graph databases and its applications

AU - Wang, Wei

AU - Wang, Chen

AU - Zhu, Yongtai

AU - Shi, Baile

AU - Pei, Jian

AU - Yan, Xifeng

AU - Han, Jiawei

PY - 2005/12/1

Y1 - 2005/12/1

N2 - Mining frequent structural patterns from graph databases is an important research problem with broad applications. Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques. The techniques have been integrated into a research prototype system - GraphMiner. In this paper, we describe a demo of GraphMiner which showcases the technical details of the index structure and the mining algorithms including their efficient implementation, the mining performance and the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications.

AB - Mining frequent structural patterns from graph databases is an important research problem with broad applications. Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques. The techniques have been integrated into a research prototype system - GraphMiner. In this paper, we describe a demo of GraphMiner which showcases the technical details of the index structure and the mining algorithms including their efficient implementation, the mining performance and the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications.

UR - http://www.scopus.com/inward/record.url?scp=29844432494&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=29844432494&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:29844432494

SP - 879

EP - 881

JO - Proceedings of the ACM SIGMOD International Conference on Management of Data

JF - Proceedings of the ACM SIGMOD International Conference on Management of Data

SN - 0730-8078

ER -