@inproceedings{cac23fc503cd459ba58718914a190ca9,
title = "Motif-based analysis of power grid robustness under attacks",
abstract = "Network motifs are often called the building blocks of networks. Analysis of motifs is found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. In the context of power grids, this phenomenon of the impact of local structure has been recently documented in fragility analysis and power system classification. At the same time, most studies of power system networks still tend to focus on global topo-logical measures of power grids, often failing to unveil hidden mechanisms behind vulnerability of real power systems and their dynamic response to malfunctions. In this paper a pilot study of motif-based analysis of power grid robustness under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of power systems.",
keywords = "Complex network, Local topological properties, Motifs, Power grids, Robustness, Subgraphs",
author = "Dey, {Asim Kumer} and Gel, {Yulia R.} and Poor, {H. Vincent}",
note = "Funding Information: ∗Yulia R. Gel has been partially supported by NSF DMS 1736368 and NSF IIS 1633331, and H. Vincent Poor has been partially supported by NSF DMS 1736417, NSF CMMI 1435778 and NSF ECCS-1549881. This material is also based in part upon work supported by DARPA. This work was initiated while the second two authors were Visiting Scholars at the Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, under the support of EPSRC grant no EP/K032208/1. The authors would like to thank M. Rosas-Casals for providing the data for European power grids and Y. Chun for help with GIS data extraction. Funding Information: Yulia R. Gel has been partially supported by NSF DMS 1736368 and NSF IIS 1633331, and H. Vincent Poor has been partially supported by NSF DMS 1736417, NSF CMMI 1435778 and NSF ECCS-1549881. This material is also based in part upon work supported by DARPA. This work was initiated while the second two authors were Visiting Scholars at the Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, under the support of EPSRC grant no EP/K032208/1. The authors would like to thank M. Rosas-Casals for providing the data for European power grids and Y. Chun for help with GIS data extraction. Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 ; Conference date: 14-11-2017 Through 16-11-2017",
year = "2018",
month = mar,
day = "7",
doi = "10.1109/GlobalSIP.2017.8309114",
language = "English (US)",
series = "2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1015--1019",
booktitle = "2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings",
address = "United States",
}