Practical Graph Mining with R

Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty

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July 15, 2013 by Chapman and Hall/CRC
Professional - 495 Pages - 168 B/W Illustrations
ISBN 9781439860847 - CAT# K12799
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Features

  • Focuses on approaches specifically for mining graph data, such as the use of graph kernels
  • Requires no prerequisites of mathematics or data mining
  • Provides numerous worked examples with R source code available online
  • Includes exercises and real-world applications at the end of each chapter
  • Offers lecture slides on the first author's website

Solutions manual available upon qualifying course adoption

Summary

Discover Novel and Insightful Knowledge from Data Represented as a Graph
Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs.

Hands-On Application of Graph Data Mining
Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks.

Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical Foundations
Every algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique.

Makes Graph Mining Accessible to Various Levels of Expertise
Assuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.