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ICASSP
2010
IEEE

Toward signal processing theory for graphs and non-Euclidean data

13 years 5 months ago
Toward signal processing theory for graphs and non-Euclidean data
Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be developed. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing framework by way of a practical example: detection of a subgraph embedded in a background graph. We describe an approach based on detection theory and provide empirical results indicating that the test statistic proposed has reasonable power to detect dense subgraphs in large random graphs.
Benjamin A. Miller, Nadya T. Bliss, Patrick J. Wol
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Benjamin A. Miller, Nadya T. Bliss, Patrick J. Wolfe
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