ABSTRACT

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 cluste

chapter 2|18 pages

An Introduction to Graph Theory

chapter 3|26 pages

An Introduction to R

chapter 4|22 pages

An Introduction to Kernel Functions

chapter 5|60 pages

Link Analysis

chapter 6|32 pages

Graph-based Proximity Measures

chapter 7|38 pages

Frequent Subgraph Mining

chapter 8|34 pages

Cluster Analysis

chapter 9|24 pages

Classification

chapter 10|48 pages

Dimensionality Reduction

chapter 11|62 pages

Graph-based Anomaly Detection

chapter 12|46 pages

Performance Metrics for Graph Mining Tasks

chapter 13|48 pages

Introduction to Parallel Graph Mining