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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11933

Title: Discovery of extreme events-related communities in contrasting groups of physical system networks
Authors: Chen, Zhengzhang
Hendrix ·, William
Guan ·, Hang
Tetteh, Isaac K.
Choudhary, Alok
Semazzi ·, Fredrick
Samatova, Nagiza F.
Keywords: Spatio-temporal data mining
Complex network analysis
· Community detection ·
· Comparative analysis·
· Network motif detection
· Extreme event prediction
Issue Date: 1-Sep-2012
Publisher: Springerlink.com
Citation: Received: 21 October 2011 / Accepted: 6 August 2012 / Published online: 1 September 2012 © The Author(s) 2012. This article is published with open access at Springerlink.com
Abstract: The latent behavior of a physical system that can exhibit extreme events such as hurricanes or rainfalls, is complex. Recently, a very promising means for studying complex systems has emerged through the concept of complex networks. Networks representing relationships between individual objects usually exhibit community dynamics. Conventional community detection methods mainly focus on either mining frequent subgraphs in a network or detecting stable communities in time-varying networks. In this paper, we formulate a novel problem—detection of predictive and phase-biased communities in contrasting groups of networks, and propose an efficient and effective machine learning solution for finding such anomalous communities. We build different groups of networks corresponding to different system’s phases, such as higher or low hurricane activity, discover phase-related system components as seeds to help bound the search space of community generation in each network, and use the proposed contrast-based technique to identify the changing communities across different groups. The detected anomalous communities are hypothesized (1) to play an important role in defining the target system’s state(s) and (2) to improve the predictive
Description: A book chapter is publihed by Received: 21 October 2011 / Accepted: 6 August 2012 / Published online: 1 September 2012 © The Author(s) 2012. This article is published with open access at Springerlink.com
URI: http://hdl.handle.net/123456789/11933
Appears in Collections:College of Science

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