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    Subgraph detection using graph signals

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    Type
    Conference Paper
    Authors
    Chepuri, Sundeep Prabhakar
    Leus, Geert
    KAUST Grant Number
    OSR-2015-Sensors-2700
    Date
    2017-03-06
    Online Publication Date
    2017-03-06
    Print Publication Date
    2016-11
    Permanent link to this record
    http://hdl.handle.net/10754/623599
    
    Metadata
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    Abstract
    In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are known, and derive an optimal Neyman-Pearson detector. Next, we derive a suboptimal detector for the case when the alternative graph is not known. The developed theory is illustrated with numerical experiments.
    Citation
    Chepuri SP, Leus G (2016) Subgraph detection using graph signals. 2016 50th Asilomar Conference on Signals, Systems and Computers. Available: http://dx.doi.org/10.1109/acssc.2016.7869097.
    Sponsors
    This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 50th Asilomar Conference on Signals, Systems and Computers
    Conference/Event name
    50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
    DOI
    10.1109/acssc.2016.7869097
    ae974a485f413a2113503eed53cd6c53
    10.1109/acssc.2016.7869097
    Scopus Count
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