Degree-based approximate sub-graph matching for social network analysis in terrorist detection
Document Type
Conference Proceeding
Publication Date
12-1-2009
Abstract
In this paper we discuss methods to find one or multiple copies of a sub-graph pattern from a bigger data graph through approximate sub-graph matching. We have compared the performance of incremental greedy method with greedy methods aided by degree information and also we have introduced the notion of a seed in incremental greedy method Simulated data based on terrorist profiles database is used in our experiment that compare the computational efficiency and matching accuracy of various methods. The experiment results suggest that with increasing size of the data graph, the efficiency advantage of degree-based method becomes more significant, while degree-based method remains as accurate as incremental greedy. The notion of seed proves to be handy when lots of noise has been introduced in the graph data. Although the run-time is longer than not having any seed, it shows improved accuracy if the attribute values are deceptively noisy.
Publication Source (Journal or Book title)
Proceedings of the 2009 International Conference on Artificial Intelligence, ICAI 2009
First Page
286
Last Page
293
Recommended Citation
Basuchowdhuri, P., Chen, J., & Chen, P. (2009). Degree-based approximate sub-graph matching for social network analysis in terrorist detection. Proceedings of the 2009 International Conference on Artificial Intelligence, ICAI 2009, 1, 286-293. Retrieved from https://repository.lsu.edu/eecs_pubs/2402