Practical Graph Mining with R

Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical 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 MiningEach 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 FoundationsEvery 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 ExpertiseAssuming 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.

Introduction Kanchana Padmanabhan, William Hendrix, and Nagiza F. SamatovaGraph Mining Applications Book Structure An Introduction to Graph Theory Stephen WareWhat Is a Graph? Vertices and Edges Comparing Graphs Directed Graphs Families of Graphs Weighted Graphs Graph Representations An Introduction to R Neil ShahWhat Is R?What Can R Do?R Packages Why Use R? Common R Functions R Installation An Introduction to Kernel Functions John JenkinsKernel Methods on Vector Data Extending Kernel Methods to Graphs Choosing Suitable Graph Kernel FunctionsKernels in This Book Link Analysis Arpan Chakraborty, Kevin Wilson, Nathan Green, Shravan Kumar Alur, Fatih Ergin, Karthik Gurumurthy, Romulo Manzano, and Deepti ChintaIntroduction Analyzing LinksMetrics 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. SigmonDefining the Proximity of Vertices in Graphs Evaluating Relatedness Using Neumann KernelsApplications Frequent Subgraph Mining Brent E. Harrison, Jason C. Smith, Stephen G. Ware, Hsiao-Wei Chen, Wenbin Chen, and Anjali KhatriAbout Frequent Subgraph MiningThe gSpan AlgorithmThe 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 ShamseldinIntroduction Minimum Spanning Tree Clustering Shared Nearest Neighbor Clustering Betweenness Centrality Clustering Highly Connected Subgraph Clustering Maximal Clique Enumeration Clustering Vertices with Kernel k-MeansApplicationHow 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. TatagiriOverview of Classification Classifcation of Vector Data: Support Vector MachinesClassifying Graphs and VerticesApplications Dimensionality Reduction Madhuri R. Marri, Lakshmi Ramachandran, Pradeep Murukannaiah, Padmashree Ravindra, Amrita Paul, Da Young Lee, David Funk, Shanmugapriya Murugappan, and William HendrixMultidimensional Scaling Kernel Principal Component AnalysisLinear Discriminant Analysis Applications Graph-Based Anomaly Detection Kanchana Padmanabhan, Zhengzhang Chen, Sriram Lakshminarasimhan, Siddarth Shankar Ramaswamy, and Bryan Thomas RichardsonTypes of AnomaliesRandom Walk Algorithm GBAD Algorithm Tensor-Based Anomaly Detection Algorithm Applications Performance Metrics for Graph Mining Tasks Kanchana Padmanabhan and John JenkinsIntroduction Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical Significance TechniquesModel 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 RenParallel 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.

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.