Practical Graph Mining with R

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

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

Biography

Nagiza F. Samatova is an associate professor of computer science at North Carolina State University and a senior research scientist at Oak Ridge National Laboratory.

"The authors provide a tour de force introduction to the different data representations (vectors, matrices), and introduce graph structures and the questions that can be answered with them. ... The book has many strong points. There is a companion website that hosts slide presentations for almost all chapters, as well the R code needed to run the example code. The impatient reader can start going through the presentations and experimenting with the code right away. The more patient reader can read the book from cover to cover. For many reader categories, this summary of existing relevant work and approaches for data mining graph structures is a welcome addition, for which the authors deserves much praise."
--Radu State, Computing Reviews