title: MAGE: Matching Approximate Patterns in Richly-Attributed Graphs.
abstract: Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multicore subgraph matching approach that supports expressive queries over large, richly-attributed graphs. Our major contributions include: (1) MAGE supports graphs with both node and edge attributes (most existing approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., matchanypermissible values), and attributes with continuous values; and (3) it is scalable, supporting graphs with several hundred million edges. We demonstrate MAGE's effectiveness and scalability via extensive experiments on large real and synthetic graphs, such as a Google+ social network with460 millionedges.
authors: College of Computing, Georgia Institute of Technology, Atlanta, GA.
authors: Robert
authors: R
authors: Pienta
authors: Department of Computer Science, Arizona Sate University, Phoenix, AZ.
authors: Acar
authors: A
authors: Tamersoy
authors: Hanghang
authors: H
authors: Tong
authors: Duen Horng
authors: DH
authors: Chau