This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book youall be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.... they fall into the same categoryadense subgraphs within books might represent genres like ascience fictiona or aromance. ... 16.1.1 Efficient Algorithms for Massive Graphs Consider a small crawl of the Web containing 1 billion vertices and 10 ... Such an algorithm will explore 1018/2 pairs, each of which will require two random accesses to extract the neighbor set. ... in polynomial time is not appropriate in the world of massive graphsathe above example shows that quadratic-timeanbsp;...
|Title||:||Mining Graph Data|
|Author||:||Diane J. Cook, Lawrence B. Holder|
|Publisher||:||John Wiley & Sons - 2006-12-18|