Negative ties network-based modeling of terrorist incidents

Dinesh C. Verma; Scott Sigmund Gartner; Diane H. Felmlee; Dave Braines

doi:10.1117/12.2558807

Negative ties network-based modeling of terrorist incidents

Dinesh C. Verma, Scott Sigmund Gartner, Diane H. Felmlee, Dave Braines

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

Abstract

Network structure represents a vital component in wide-ranging aspects of Multi-Domain Operations (MDO). One specific type of network that holds promise in understanding the behavior of complex environments such as MDO consists of ones where nodes are combined with both positive ties and negative ties. Positive ties are edges that promote nodes to become similar to each other, or homophilous, while negative ties are edges that promote nodes to be dissimilar to each other. Such a model of influence among the nodes can be used to explain various phenomena happening within a society, modeling peer influences, spread of memes, or to model incidents of violence. In this paper, we propose a Positive-Negative tie network model to analyze terrorism incidents in India, and investigate the role of this network in general network classification and situation understanding contexts.

Original language	English (US)
Title of host publication	Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
Editors	Tien Pham, Latasha Solomon, Katie Rainey
Publisher	SPIE
ISBN (Electronic)	9781510636033
DOIs	https://doi.org/10.1117/12.2558807
State	Published - 2020
Event	Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II 2020 - Virtual, Online, United States Duration: Apr 27 2020 → May 8 2020

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11413
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II 2020
Country/Territory	United States
City	Virtual, Online
Period	4/27/20 → 5/8/20

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2558807

Cite this

Verma, D. C., Gartner, S. S., Felmlee, D. H., & Braines, D. (2020). Negative ties network-based modeling of terrorist incidents. In T. Pham, L. Solomon, & K. Rainey (Eds.), Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II Article 114131S (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11413). SPIE. https://doi.org/10.1117/12.2558807

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title = "Negative ties network-based modeling of terrorist incidents",

abstract = "Network structure represents a vital component in wide-ranging aspects of Multi-Domain Operations (MDO). One specific type of network that holds promise in understanding the behavior of complex environments such as MDO consists of ones where nodes are combined with both positive ties and negative ties. Positive ties are edges that promote nodes to become similar to each other, or homophilous, while negative ties are edges that promote nodes to be dissimilar to each other. Such a model of influence among the nodes can be used to explain various phenomena happening within a society, modeling peer influences, spread of memes, or to model incidents of violence. In this paper, we propose a Positive-Negative tie network model to analyze terrorism incidents in India, and investigate the role of this network in general network classification and situation understanding contexts.",

author = "Verma, {Dinesh C.} and Gartner, {Scott Sigmund} and Felmlee, {Diane H.} and Dave Braines",

note = "Funding Information: This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001 and the Penn State Center for Security Research and Education. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Publisher Copyright: {\textcopyright} 2020 SPIE.; Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II 2020 ; Conference date: 27-04-2020 Through 08-05-2020",

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doi = "10.1117/12.2558807",

language = "English (US)",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

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editor = "Tien Pham and Latasha Solomon and Katie Rainey",

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Verma, DC, Gartner, SS, Felmlee, DH & Braines, D 2020, Negative ties network-based modeling of terrorist incidents. in T Pham, L Solomon & K Rainey (eds), Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II., 114131S, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11413, SPIE, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II 2020, Virtual, Online, United States, 4/27/20. https://doi.org/10.1117/12.2558807

Negative ties network-based modeling of terrorist incidents. / Verma, Dinesh C.; Gartner, Scott Sigmund; Felmlee, Diane H. et al.
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II. ed. / Tien Pham; Latasha Solomon; Katie Rainey. SPIE, 2020. 114131S (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11413).

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

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N1 - Funding Information: This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001 and the Penn State Center for Security Research and Education. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Publisher Copyright: © 2020 SPIE.

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N2 - Network structure represents a vital component in wide-ranging aspects of Multi-Domain Operations (MDO). One specific type of network that holds promise in understanding the behavior of complex environments such as MDO consists of ones where nodes are combined with both positive ties and negative ties. Positive ties are edges that promote nodes to become similar to each other, or homophilous, while negative ties are edges that promote nodes to be dissimilar to each other. Such a model of influence among the nodes can be used to explain various phenomena happening within a society, modeling peer influences, spread of memes, or to model incidents of violence. In this paper, we propose a Positive-Negative tie network model to analyze terrorism incidents in India, and investigate the role of this network in general network classification and situation understanding contexts.

AB - Network structure represents a vital component in wide-ranging aspects of Multi-Domain Operations (MDO). One specific type of network that holds promise in understanding the behavior of complex environments such as MDO consists of ones where nodes are combined with both positive ties and negative ties. Positive ties are edges that promote nodes to become similar to each other, or homophilous, while negative ties are edges that promote nodes to be dissimilar to each other. Such a model of influence among the nodes can be used to explain various phenomena happening within a society, modeling peer influences, spread of memes, or to model incidents of violence. In this paper, we propose a Positive-Negative tie network model to analyze terrorism incidents in India, and investigate the role of this network in general network classification and situation understanding contexts.

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M3 - Conference contribution

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ER -

Negative ties network-based modeling of terrorist incidents

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