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Discriminative Frequent Pattern-Based Graph Classification

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Link Mining: Models, Algorithms, and Applications

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.

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Author information

Authors and Affiliations

  1. The Chinese University of Hong Kong, Shatin, N.T, Hong Kong

    Hong Cheng

  2. University of California at Santa Barbara, Santa Barbara, CA, USA

    Xifeng Yan

  3. UIUC, Urbana, IL, USA

    Jiawei Han

Authors
  1. Hong Cheng
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  2. Xifeng Yan
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  3. Jiawei Han
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Corresponding author

Correspondence to Xifeng Yan .

Editor information

Editors and Affiliations

  1. Dept. Computer Science, University of Illinois, Chicago, S. Morgan St. 851, Chicago, 60607-7053, Illinois, USA

    Philip S. Yu

  2. Dept. Computer Science, University of Illinois, Urbana-Champaign, N. Goodwin Ave. 201, Urbana, 61801, Illinois, USA

    Jiawei Han

  3. School of Computer Science, Carnegie Mellon University, Forbes Ave. 5000, Pittsburgh, 15213, Pennsylvania, USA

    Christos Faloutsos

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Cheng, H., Yan, X., Han, J. (2010). Discriminative Frequent Pattern-Based Graph Classification. In: Yu, P., Han, J., Faloutsos, C. (eds) Link Mining: Models, Algorithms, and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6515-8_9

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