@inproceedings{63bdfd9ac3bc4ae78f728cb2fd4853a9,
title = "Matched filtering for subgraph detection in dynamic networks",
abstract = "Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic setting a previous method for the detection of anomalously dense subgraphs in a large background. In simulation, we show that this temporal integration technique enables the detection of weak subgraph anomalies than are not detectable in the static case. We also demonstrate background/foreground separation using a real background graph based on a computer network.",
keywords = "community detection, dynamic graphs, graph algorithms, matched filtering, signal detection theory",
author = "Miller, {Benjamin A.} and Beard, {Michelle S.} and Bliss, {Nadya T.}",
year = "2011",
doi = "10.1109/SSP.2011.5967745",
language = "English (US)",
isbn = "9781457705700",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
pages = "509--512",
booktitle = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011",
note = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011 ; Conference date: 28-06-2011 Through 30-06-2011",
}