MAGE: Matching approximate patterns in richly-attributed graphs

Robert Pienta, Acar Tamersoy, Hanghang Tong, Duen Horng Chau
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="" style="color: black;">2014 IEEE International Conference on Big Data (Big Data)</a> </i> &nbsp;
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
more &raquo; ... approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., match any permissible 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 with 460 million edges.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1109/bigdata.2014.7004278</a> <a target="_blank" rel="external noopener" href="">pmid:25859565</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC4388251</a> <a target="_blank" rel="external noopener" href="">dblp:conf/bigdataconf/PientaTTC14</a> <a target="_blank" rel="external noopener" href="">fatcat:gv5ejegbvfez7jzdfegsgxq4o4</a> </span>
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