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Toward signal processing theory for graphs and non-Euclidean data
| Content Provider | CiteSeerX |
|---|---|
| Author | Miller, Benjamin A. Bliss, Nadya T. Wolfe, Patrick J. |
| Description | in Proc. ICASSP, 2010 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 de-veloped. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing frame-work by way of a practical example: detection of a subgraph em-bedded 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. Index Terms—Chi-squared test, community detection, graph algorithms, high-dimensional data, signal detection theory 1. |
| File Format | |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Canonical Example Conceptual Advantage Many Field Detection Theory Practical Example Reasonable Power High-dimensional Data Large Random Graph Classical Euclidean Setting Dense Subgraphs Non-euclidean Data Signal Processing Theory Common Data Structure Graph Analysis Problem Toward Signal Processing Theory Graph Algorithm Index Term Chi-squared Test Community Detection High-dimensional Non-euclidean Data Set Many Algorithm Signal Processing Frame-work Test Statistic Signal Detection Theory Background Graph Empirical Result |
| Content Type | Text |
| Resource Type | Article |
