ANF: a fast and scalable tool for data mining in massive graphs

Graphs are an increasingly important data source, with such important graphs as the Internet and the Web. Other familiar graphs include CAD circuits, phone records, gene sequences, city streets, social networks and academic citations. Any kind of relationship, such as actors appearing in movies, can be represented as a graph. This work presents a data mining tool, called ANF, that can quickly answer a number of interesting questions on graph-represented data, such as the following. How robust is the Internet to failures? What are the most influential database papers? Are there gender differences in movie appearance patterns? At its core, ANF is based on a fast and memory-efficient approach for approximating the complete "neighbourhood function" for a graph. For the Internet graph (268K nodes), ANF's highlyaccurate approximation is more than 700 times faster than the exact computation. This reduces the running time from nearly a day to a matter of a minute or two, allowi...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Christopher R. Palmer, Phillip B. Gibbons, Christos Faloutsos
• Keywords: Data Mining, Disk Resident Graphs, Important Graphs, KDD 2002, Other Familiar Graphs