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

ASJC Scopus subject areas

  • Medicine(all)

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  • 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