TY - GEN
T1 - GRAPHITE
T2 - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
AU - Chau, Duen Horng
AU - Faloutsos, Christos
AU - Tong, Hanghang
AU - Hong, Jason I.
AU - Gallagher, Brian
AU - Eliassi-Rad, Tina
PY - 2008
Y1 - 2008
N2 - We present Graphite, a system that allows the user to visually construct a query pattern, finds both its exact and approximate matching subgraphs in large attributed graphs, and visualizes the matches. For example, in a social network where a person's occupation is an attribute, the user can draw a'star' query for "finding a CEO who has interacted with a Secretary, a Manager, and an Accountant, or a structure very similar to this". Graphite uses the G-Ray algorithm to run the query against a user-chosen data graph, gaining all of its benefits, namely its high speed, scalability, and its ability to find both exact and near matches. Therefore, for the example above, Graphite tolerates indirect paths between, say, the CEO and the Accountant, when no direct path exists. Graphite uses fast algorithms to estimate node proximities when finding matches, enabling it to scale well with the graph database size. We demonstrate Graphite's usage and benefits using the DBLP author-publication graph, which consists of 356K nodes and 1.9M edges. A demo video of Graphite can be downloaded at http://www.cs.cmu.edu/~dchau/graphite/graphite.mov.
AB - We present Graphite, a system that allows the user to visually construct a query pattern, finds both its exact and approximate matching subgraphs in large attributed graphs, and visualizes the matches. For example, in a social network where a person's occupation is an attribute, the user can draw a'star' query for "finding a CEO who has interacted with a Secretary, a Manager, and an Accountant, or a structure very similar to this". Graphite uses the G-Ray algorithm to run the query against a user-chosen data graph, gaining all of its benefits, namely its high speed, scalability, and its ability to find both exact and near matches. Therefore, for the example above, Graphite tolerates indirect paths between, say, the CEO and the Accountant, when no direct path exists. Graphite uses fast algorithms to estimate node proximities when finding matches, enabling it to scale well with the graph database size. We demonstrate Graphite's usage and benefits using the DBLP author-publication graph, which consists of 356K nodes and 1.9M edges. A demo video of Graphite can be downloaded at http://www.cs.cmu.edu/~dchau/graphite/graphite.mov.
UR - http://www.scopus.com/inward/record.url?scp=62449175729&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62449175729&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2008.99
DO - 10.1109/ICDMW.2008.99
M3 - Conference contribution
AN - SCOPUS:62449175729
SN - 9780769535036
T3 - Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
SP - 963
EP - 966
BT - Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Y2 - 15 December 2008 through 19 December 2008
ER -