TY - GEN
T1 - Discovering patterns in social networks with graph matching algorithms
AU - Ogaard, Kirk
AU - Roy, Heather
AU - Kase, Sue
AU - Nagi, Rakesh
AU - Sambhoos, Kedar
AU - Sudit, Moises
PY - 2013
Y1 - 2013
N2 - Social media data are amenable to representation by directed graphs. A node represents an entity in the social network such as a person, organization, location, or event. A link between two nodes represents a relationship such as communication, participation, or financial support. When stored in a database, these graphs can be searched and analyzed for occurrences of various subgraph patterns of nodes and links. This paper describes an interactive visual interface for constructing subgraph patterns called the Graph Matching Toolkit (GMT). GMT searches for subgraph patterns using the Truncated Search Tree (TruST) graph matching algorithm. GMT enables an analyst to draw a subgraph pattern and assign labels to nodes and links using a mouse and drop-down menus. GMT then executes the TruST algorithm to find subgraph pattern occurrences within the directed graph. Preliminary results using GMT to analyze a simulated collection of text communications containing a terrorist plot are reported.
AB - Social media data are amenable to representation by directed graphs. A node represents an entity in the social network such as a person, organization, location, or event. A link between two nodes represents a relationship such as communication, participation, or financial support. When stored in a database, these graphs can be searched and analyzed for occurrences of various subgraph patterns of nodes and links. This paper describes an interactive visual interface for constructing subgraph patterns called the Graph Matching Toolkit (GMT). GMT searches for subgraph patterns using the Truncated Search Tree (TruST) graph matching algorithm. GMT enables an analyst to draw a subgraph pattern and assign labels to nodes and links using a mouse and drop-down menus. GMT then executes the TruST algorithm to find subgraph pattern occurrences within the directed graph. Preliminary results using GMT to analyze a simulated collection of text communications containing a terrorist plot are reported.
KW - graph matching
KW - social network analysis
KW - visualization software
UR - http://www.scopus.com/inward/record.url?scp=84874804910&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874804910&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37210-0_37
DO - 10.1007/978-3-642-37210-0_37
M3 - Conference contribution
AN - SCOPUS:84874804910
SN - 9783642372094
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 341
EP - 349
BT - Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings
T2 - 6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013
Y2 - 2 April 2013 through 5 April 2013
ER -