@inproceedings{62641f65d5c549dc97a5609238b58c74,
title = "Toward signal processing theory for graphs and non-Euclidean data",
abstract = "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.",
keywords = "Chi-squared test, Community detection, Graph algorithms, High-dimensional data, Signal detection theory",
author = "Miller, {Benjamin A.} and Bliss, {Nadya T.} and Wolfe, {Patrick J.}",
year = "2010",
doi = "10.1109/ICASSP.2010.5494930",
language = "English",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "5414--5417",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}