Title:
Mage: Expressive Pattern Matching in Richly-Attributed Graphs
Mage: Expressive Pattern Matching in Richly-Attributed Graphs
Author(s)
Pienta, Robert
Tamersoy, Acar
Tong, Hanghang
Chau, Duen Horng
Tamersoy, Acar
Tong, Hanghang
Chau, Duen Horng
Advisor(s)
Editor(s)
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Abstract
Given a large graph with millions of nodes and edges, say a social
graph where both the nodes and edges can have multiple different
kinds of attributes (e.g., job titles, tie strengths), how do we quickly
find matches for subgraphs of interest (e.g., a ring of businessmen
with strong ties)? We propose MAGE,
Multiple Attribute Graph Engine, a subgraph matching framework that pushes the envelope of graph
matching capabilities and performance, through several major innovations: (i) with
line graph
transformation, MAGE works for graphs
with both node and edge attributes and return both exact as well as
near matches — other techniques often support only node attributes
and return only exact matches; (ii) MAGE supports a plethora of
queries, including multiple attributes for each node or edge, wild-cards as attribute values (i.e., match any
permissible value), and continuous attributes via multiple discretization strategies; (iii) MAGE
leverages a novel technique based on
memory mapping
to compute
random walk with restart probabilities, which provides a speedup
of more than
2 orders of magnitude on large graphs. We evaluated
MAGE’s effectiveness and scalability with real and synthetic graphs
with up to 2.3
million edges. Experimental results on the DBLP authorship graph and the Rotten Tomatoes movie graph illustrate the
effectiveness and exploratory functionality of our contributions to
graph querying. By devising query-centric innovations, our work improves the ease with which a user can explore their graph data.
Sponsor
Date Issued
2013
Extent
Resource Type
Text
Resource Subtype
Technical Report