基于Mapreduce的大规模图强连通分量算法，吕璐，谢磊，有向图强连通分量是图论中的基本问题。强连通分量算法一般都是基于深度优先搜索，但难于在大规模图上并行实现。本文提出了一种基

格米 GraMi 是一种在单个大图中进行频繁子图挖掘的新框架，GraMi 比现有技术高出两个数量级。 GraMi 支持查找频繁子图和频繁模式，与子图相比，模式提供了更强大的匹配版本，可以捕获图节点（如朋友的朋友）之间的传递交互，这​​在现代应用程序中非常常见。 此外，GraMi 支持对结果以及近似结果的用户定义结构和语义约束。 有关更多详细信息，请查看我们的论文：Mohammed Elseidy、Ehab Abdelhamid、Spiros Skiadopoulos 和 Panos Kalnis。 “ GRAMI：单个大图中的频繁子图和模式挖掘。PVLDB，7（7）：517-528，2014年。” 内容： README ................... This file LICENSE.txt .............. License file (Open Sourc

Graph mining and management has become an important topic of research re- cently because of numerous applications to a wide variety of data mining prob- lems in computational biology, chemical data analysis, drug discovery and com- munication networking. Traditional data mining and management algorithms such as clustering, classification, frequent pattern mining and indexing have now been extended to the graph scenario. This book contains a number of chapters which are carefully chosen in order to discuss the broad research issues in graph management and mining. In addition, a number of important applications of graph mining are also covered in the book. The purpose of this chapter is to provide an overview of the different kinds of graph processing and mining tech- niques, and the coverage of these topics in this book.