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Activity Number:
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315
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #318698
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View Presentation
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Title:
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Hypothesis Testing for Detecting Changes Within a Barabási-Albert Network
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Author(s):
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Fairul Mohd-Zaid* and Christine Schubert Kabban and Edward White and Richard Deckro
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Companies:
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Air Force Research Lab and Air Force Institute of Technology and Air Force Institute of Technology and Air Force Institute of Technology
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Keywords:
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Barabási-Albert ;
Classification ;
Hypothesis Test ;
Power Law ;
Networks ;
L-moments
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Abstract:
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A hypothesis test for detecting changes within the Barabási-Albert network is proposed. We characterize change as a proportion of edge deletion based upon the degree distribution for the nodes within the network. The degree distribution of the network is assumed to follow the Pareto distribution. Therefore, a statistical test based on the joint distribution of multiple L-moments for the Pareto distribution is developed. Simulation results examined the power of this test for various network sizes and proportion of edge deletion. Sensitivity of the proportion of deleted edges to nodal degree was investigated using general categories of small, medium, and large degree nodes. These categories were defined by percentiles of the degree distribution for each network size examined. Results demonstrate that this test has high power for detecting changes even for small networks (32 nodes) and when a small proportion of edges are deleted (e.g. 0.01). Implications for these results suggest that this test may be utilized for early detection of changes occurring within the Barabási-Albert network model which may be too subtle for tests based upon lower moments such as the mean.
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Authors who are presenting talks have a * after their name.
