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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References
Walsh, D.: Relooking the JDL Model for Fusion on a Global Graph. In: National Symposium for Sensor Data Fusion (2010)
Sambhoos, K., Nagi, R., Sudit, M., Stotz, A.: Enhancements to High Level Data Fusion using Graph Matching and State Space Search. Information Fusion (2009) (in press, corrected proof )
Sambhoos, K.: Graph Matching Applications in High Level Information Fusion [Dissertation]. State University of New York at Buffalo, Buffalo (2007)
Sudit, M., Nagi, R., Stotz, A., Sambhoos, K.: A Graph-Based Framework for Fusion: From Hypothesis Generation to Forensics. In: 9th International Conference on Information Fusion. IEEE Press, New York (2006)
Zhang, W.: Depth-First Branch-and-Bound versus Local Search: A Case Study. In: 17th National Conference on Artificial Intelligence, pp. 930–935. AAAI Press, Palo Alto (2000)
Guha, D., Chakraborty, D.: A New Approach to Fuzzy Distance Measure and Similarity Measure between Two Generalized Fuzzy Numbers. Applied Soft Computing 10(1), 90–99 (2010)
Chen, S., Chen, J.: Fuzzy Risk Analysis Based on Ranking Generalized Fuzzy Numbers with Different Heights and Different Spreads. Expert Systems with Applications 36(3), 6833–6842 (2009)
Mittrick, M., Roy, H., Kase, S., Bowman, E.: Refinement of the Ali Baba Data Set. U.S. Army Research Laboratory, ARL-TN-0476 (2012)
Knuth, D.: The Art of Computer Programming, 3rd edn. Fundamental Algorithms, vol. 1. Addison Wesley Longman Publishing Co. Inc., Redwood City (1997)
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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
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