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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.

Assuming no prior knowledge of mathematics or data mining, this self-contained book presents a "do-it-yourself" approach to extracting interesting patterns from graph data. Each chapter focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through many applications, the book demonstrates how computational techniques can help solve real-world problems. Every algorithm and example is accompanied with R code, allowing readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice.

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Introduction Kanchana Padmanabhan, William Hendrix, and Nagiza F. Samatova
Graph Mining Applications 
Book Structure 

An Introduction to Graph Theory Stephen Ware
What Is a Graph? 
Vertices and Edges 
Comparing Graphs 
Directed Graphs 
Families of Graphs 
Weighted Graphs 
Graph Representations

An Introduction to R Neil Shah
What Is R?
What Can R Do?
R Packages 
Why Use R? 
Common R Functions 
R Installation

An Introduction to Kernel Functions John Jenkins
Kernel Methods on Vector Data 
Extending Kernel Methods to Graphs 
Choosing Suitable Graph Kernel Functions
Kernels in This Book

Link Analysis Arpan Chakraborty, Kevin Wilson, Nathan Green, Shravan Kumar Alur, Fatih Ergin, Karthik Gurumurthy, Romulo Manzano, and Deepti Chinta
Introduction 
Analyzing Links
Metrics for Analyzing Networks 
The PageRank Algorithm 
Hyperlink-Induced Topic Search (HITS)
Link Prediction 
Applications 


Graph-Based Proximity Measures Kevin A. Wilson, Nathan D. Green, Laxmikant Agrawal, Xibin Gao, Dinesh Madhusoodanan, Brian Riley, and James P. Sigmon
Defining the Proximity of Vertices in Graphs 
Evaluating Relatedness Using Neumann Kernels
Applications
Frequent Subgraph Mining Brent E. Harrison, Jason C. Smith, Stephen G. Ware, Hsiao-Wei Chen, Wenbin Chen, and Anjali Khatri
About Frequent Subgraph Mining
The gSpan Algorithm
The SUBDUE Algorithm 
Mining Frequent Subtrees with SLEUTH 
Applications

Cluster Analysis Kanchana Padmanabhan, Brent Harrison, Kevin Wilson, Michael L. Warren, Katie Bright, Justin Mosiman, Jayaram Kancherla, Hieu Phung, Benjamin Miller, and Sam Shamseldin
Introduction 
Minimum Spanning Tree Clustering 
Shared Nearest Neighbor Clustering 
Betweenness Centrality Clustering 
Highly Connected Subgraph Clustering 
Maximal Clique Enumeration 
Clustering Vertices with Kernel k-Means
Application
How to Choose a Clustering Technique 
Classification Srinath Ravindran, John Jenkins, Huseyin Sencan, Jay Prakash Goel, Saee Nirgude, Kalindi K. Raichura, Suchetha M. Reddy, and Jonathan S. Tatagiri
Overview of Classification 
Classifcation of Vector Data: Support Vector Machines 
Classifying Graphs and Vertices
Applications
Dimensionality Reduction Madhuri R. Marri, Lakshmi Ramachandran, Pradeep Murukannaiah, Padmashree Ravindra, Amrita Paul, Da Young Lee, David Funk, Shanmugapriya Murugappan, and William Hendrix
Multidimensional Scaling 
Kernel Principal Component Analysis
Linear Discriminant Analysis 
Applications


Graph-Based Anomaly Detection Kanchana Padmanabhan, Zhengzhang Chen, Sriram Lakshminarasimhan, Siddarth Shankar Ramaswamy, and Bryan Thomas Richardson
Types of Anomalies
Random Walk Algorithm 
GBAD Algorithm 
Tensor-Based Anomaly Detection Algorithm 
Applications
Performance Metrics for Graph Mining Tasks Kanchana Padmanabhan and John Jenkins
Introduction 
Supervised Learning Performance Metrics 
Unsupervised Learning Performance Metrics 
Optimizing Metrics 
Statistical Significance Techniques
Model Comparison 
Handling the Class Imbalance Problem in Supervised Learning 
Other Issues 
Application Domain-Specific Measures


Introduction to Parallel Graph Mining William Hendrix, Mekha Susan Varghese, Nithya Natesan, Kaushik Tirukarugavur Srinivasan, Vinu Balajee, and Yu Ren
Parallel Computing Overview 
Embarassingly Parallel Computation 
Calling Parallel Codes in R 
Creating Parallel Codes in R Using Rmpi 
Practical Issues in Parallel Programming 
Index

Exercises and Bibliography appear at the end of each chapter.

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