Mining Graph Data

Edited by:

Diane J. Cook, Washington State University
Lawrence B. Holder, Washington State University

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Synopsis

Much of data mining research is focused on algorithms that can discover concepts in non-relational data represented using only an entity's attributes. However, much of the data being collected is relational, or structural, in nature, requiring tools for the analysis and discovery of concepts in structural data. Graphs provide a natural representation for many of these structured data applications. Mining graph data introduces a number of algorithmic challenges. Solutions to these challenges are becoming an increasingly popular focus of study.

This book will report current work from researchers and practitioners working on theoretical and practical aspects of the graph-based data mining problem. Topics will include relevant background on graph theory, techniques for discovery of patterns and discrimination between classes from data represented using graphs, practical tools for manipulating and visualizing graphs, and a sample of practical graph-based data mining applications.

Graph Data Testbed

One unique aspect of this book is our intent to have each contributor evaluate their graph data mining or visualization technique on a testbed of graphs. To that end, we have compiled below a list of potential data for this purpose. See Graph Datasets for more information.