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MAGE: Matching Approximate Patterns in Richly-Attributed Graphs.

Robert Pienta1, Acar Tamersoy2, Hanghang Tong

  • 1College of Computing, Georgia Institute of Technology, Atlanta, GA.

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|April 11, 2015
PubMed
Summary
This summary is machine-generated.

Finding specific patterns in large, attribute-rich graphs is now faster with MAGE (Multicore Approach for Graph Exploration). This scalable method efficiently matches subgraphs using complex queries over both node and edge attributes.

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Area of Science:

  • Computer Science
  • Data Science
  • Graph Databases

Background:

  • Analyzing large, attribute-rich graphs presents significant computational challenges.
  • Existing subgraph matching methods often lack support for both node and edge attributes or expressive query capabilities.

Purpose of the Study:

  • To introduce MAGE, a novel multicore approach for scalable subgraph matching.
  • To enable efficient querying of large graphs with complex node and edge attributes.

Main Methods:

  • Developed MAGE, a multicore subgraph matching algorithm.
  • Implemented support for node and edge attributes, including wildcards and continuous values.
  • Designed for scalability on graphs with millions to hundreds of millions of edges.

Main Results:

  • MAGE successfully handles graphs with both node and edge attributes, a limitation in many existing systems.
  • The approach supports expressive queries, including multiple edge attributes and wildcard matching.
  • Demonstrated scalability and effectiveness on large real-world and synthetic graph datasets.

Conclusions:

  • MAGE provides a scalable and effective solution for subgraph matching in richly-attributed graphs.
  • The method significantly advances the ability to query and analyze complex network data.
  • MAGE is suitable for applications involving large-scale graph analysis, such as social network analysis.