Abstract
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.
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© 2013 Springer-Verlag Berlin Heidelberg
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Ogaard, K., Roy, H., Kase, S., Nagi, R., Sambhoos, K., Sudit, M. (2013). Discovering Patterns in Social Networks with Graph Matching Algorithms. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_37
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DOI: https://doi.org/10.1007/978-3-642-37210-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37209-4
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