Discriminative frequent pattern-based graph classification

Hong Cheng, Xifeng Yan, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Frequent graph mining has been studied extensively with many scalable graph mining algorithms developed in the past. Graph patterns are essential not only for exploratory graph mining but also for advanced graph analysis tasks such as graph indexing, graph clustering, and graph classification. In this chapter, we examine the frequent pattern-based classification of graph data. We will introduce different types of patterns used in graph classification and their efficient mining approaches. These approaches could directly mine the most discriminative subgraphs without enumerating the complete set of frequent graph patterns. The application of graph classification into chemical compound analysis and software behavior prediction will be discussed to demonstrate the power of discriminative subgraphs.

Original languageEnglish (US)
Title of host publicationLink Mining
Subtitle of host publicationModels, Algorithms, and Applications
PublisherSpringer New York
Pages237-262
Number of pages26
Volume9781441965158
ISBN (Electronic)9781441965158
ISBN (Print)9781441965141
DOIs
StatePublished - Jan 1 2010

Fingerprint

Cluster Analysis
Software

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Cheng, H., Yan, X., & Han, J. (2010). Discriminative frequent pattern-based graph classification. In Link Mining: Models, Algorithms, and Applications (Vol. 9781441965158, pp. 237-262). Springer New York. https://doi.org/10.1007/978-1-4419-6515-8-9

Discriminative frequent pattern-based graph classification. / Cheng, Hong; Yan, Xifeng; Han, Jiawei.

Link Mining: Models, Algorithms, and Applications. Vol. 9781441965158 Springer New York, 2010. p. 237-262.

Research output: Chapter in Book/Report/Conference proceedingChapter

Cheng, H, Yan, X & Han, J 2010, Discriminative frequent pattern-based graph classification. in Link Mining: Models, Algorithms, and Applications. vol. 9781441965158, Springer New York, pp. 237-262. https://doi.org/10.1007/978-1-4419-6515-8-9
Cheng H, Yan X, Han J. Discriminative frequent pattern-based graph classification. In Link Mining: Models, Algorithms, and Applications. Vol. 9781441965158. Springer New York. 2010. p. 237-262 https://doi.org/10.1007/978-1-4419-6515-8-9
Cheng, Hong ; Yan, Xifeng ; Han, Jiawei. / Discriminative frequent pattern-based graph classification. Link Mining: Models, Algorithms, and Applications. Vol. 9781441965158 Springer New York, 2010. pp. 237-262
@inbook{5417e72057bb41f9a0790e1f07c7ec7f,
title = "Discriminative frequent pattern-based graph classification",
abstract = "Frequent graph mining has been studied extensively with many scalable graph mining algorithms developed in the past. Graph patterns are essential not only for exploratory graph mining but also for advanced graph analysis tasks such as graph indexing, graph clustering, and graph classification. In this chapter, we examine the frequent pattern-based classification of graph data. We will introduce different types of patterns used in graph classification and their efficient mining approaches. These approaches could directly mine the most discriminative subgraphs without enumerating the complete set of frequent graph patterns. The application of graph classification into chemical compound analysis and software behavior prediction will be discussed to demonstrate the power of discriminative subgraphs.",
author = "Hong Cheng and Xifeng Yan and Jiawei Han",
year = "2010",
month = "1",
day = "1",
doi = "10.1007/978-1-4419-6515-8-9",
language = "English (US)",
isbn = "9781441965141",
volume = "9781441965158",
pages = "237--262",
booktitle = "Link Mining",
publisher = "Springer New York",

}

TY - CHAP

T1 - Discriminative frequent pattern-based graph classification

AU - Cheng, Hong

AU - Yan, Xifeng

AU - Han, Jiawei

PY - 2010/1/1

Y1 - 2010/1/1

N2 - Frequent graph mining has been studied extensively with many scalable graph mining algorithms developed in the past. Graph patterns are essential not only for exploratory graph mining but also for advanced graph analysis tasks such as graph indexing, graph clustering, and graph classification. In this chapter, we examine the frequent pattern-based classification of graph data. We will introduce different types of patterns used in graph classification and their efficient mining approaches. These approaches could directly mine the most discriminative subgraphs without enumerating the complete set of frequent graph patterns. The application of graph classification into chemical compound analysis and software behavior prediction will be discussed to demonstrate the power of discriminative subgraphs.

AB - Frequent graph mining has been studied extensively with many scalable graph mining algorithms developed in the past. Graph patterns are essential not only for exploratory graph mining but also for advanced graph analysis tasks such as graph indexing, graph clustering, and graph classification. In this chapter, we examine the frequent pattern-based classification of graph data. We will introduce different types of patterns used in graph classification and their efficient mining approaches. These approaches could directly mine the most discriminative subgraphs without enumerating the complete set of frequent graph patterns. The application of graph classification into chemical compound analysis and software behavior prediction will be discussed to demonstrate the power of discriminative subgraphs.

UR - http://www.scopus.com/inward/record.url?scp=84906730192&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906730192&partnerID=8YFLogxK

U2 - 10.1007/978-1-4419-6515-8-9

DO - 10.1007/978-1-4419-6515-8-9

M3 - Chapter

AN - SCOPUS:84906730192

SN - 9781441965141

VL - 9781441965158

SP - 237

EP - 262

BT - Link Mining

PB - Springer New York

ER -