Anomalous subgraph detection in publication networks: Leveraging truth

Nadya Bliss; B. R Erick Peirson; Deryc Painter; Manfred Laubichler

doi:10.1109/ACSSC.2014.7094823

Anomalous subgraph detection in publication networks: Leveraging truth

Nadya Bliss, B. R Erick Peirson, Deryc Painter, Manfred Laubichler

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.

Original language	English (US)
Title of host publication	Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	2005-2009
Number of pages	5
ISBN (Electronic)	9781479982974
DOIs	https://doi.org/10.1109/ACSSC.2014.7094823
State	Published - Apr 24 2015
Event	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 2 2014 → Nov 5 2014

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2015-April

Other

Other	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	11/2/14 → 11/5/14

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2014.7094823

Cite this

Bliss, N., Peirson, B. R. E., Painter, D., & Laubichler, M. (2015). Anomalous subgraph detection in publication networks: Leveraging truth. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 2005-2009). Article 7094823 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094823

Anomalous subgraph detection in publication networks: Leveraging truth. / Bliss, Nadya; Peirson, B. R Erick; Painter, Deryc et al.
Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. ed. / Michael B. Matthews. IEEE Computer Society, 2015. p. 2005-2009 7094823 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Bliss, N, Peirson, BRE, Painter, D & Laubichler, M 2015, Anomalous subgraph detection in publication networks: Leveraging truth. in MB Matthews (ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers., 7094823, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2015-April, IEEE Computer Society, pp. 2005-2009, 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/2/14. https://doi.org/10.1109/ACSSC.2014.7094823

Bliss N, Peirson BRE, Painter D, Laubichler M. Anomalous subgraph detection in publication networks: Leveraging truth. In Matthews MB, editor, Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. IEEE Computer Society. 2015. p. 2005-2009. 7094823. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2014.7094823

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AB - Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.

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Anomalous subgraph detection in publication networks: Leveraging truth

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