Managing and mining large graphs: patterns and algorithms

Graphs are everywhere: social networks, the World Wide Web, biological networks, and many more. The sizes of graphs are growing at unprecedented rate, spanning millions and billions of nodes and edges. What are the patterns in large graphs, spanning Giga, Tera, and heading toward Peta bytes? What are the best tools, and how can they help us solve graph mining problems? How do we scale up algorithms for handling graphs with billions of nodes and edges? These are exactly the goals of this tutorial. We start with the patterns in real-world static, weighted, and dynamic graphs. Then we describe important tools for large graph mining, including singular value decomposition, and Hadoop. Finally, we conclude with the design and the implementation of scalable graph mining algorithms on Hadoop. This tutorial is complementary to the related tutorial "Managing and Mining Large Graphs: Systems and Implementations".