Complex networks for terrorist target prediction

Gian Maria Campedelli; Iain Cruickshank; Kathleen M. Carley

doi:10.1007/978-3-319-93372-6_38

Complex networks for terrorist target prediction

Gian Maria Campedelli^*, Iain Cruickshank, Kathleen M. Carley

^*Autore corrispondente per questo lavoro

Carnegie Mellon University

Risultato della ricerca: Contributo in libro › Contributo a convegno

2 Citazioni (Scopus)

Abstract

Developments in statistics and computer science have influenced research on many social problems. This process also applies to the study of terrorism. In this context, network analysis is one of the most popular mathematical methods for analyzing terrorist organizations and dynamics. Nonetheless, few studies have applied network science to the analysis of terrorist events. Therefore, in this work we first introduce a novel method to analyze the heterogeneous dynamics of terrorist attacks through the creation of a dynamic meta-network of terror for the period 1997–2016. Second, we use our terrorist meta-network to test the power of Network-based Inference algorithm in predicting terrorist targets. Results are promising and show how this algorithm reaches high levels of precision, accuracy, and recall and indicate that network outcomes can be used in broader machine learning models.

Lingua originale	English
Titolo della pubblicazione ospite	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagine	348-353
Numero di pagine	6
Volume	10899
DOI	https://doi.org/10.1007/978-3-319-93372-6_38
Stato di pubblicazione	Pubblicato - 2018
Pubblicato esternamente	Sì
Evento	11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018 - Washington, DC, USA Durata: 10 lug 2018 → 13 lug 2018

Serie di pubblicazioni

Nome	LECTURE NOTES IN COMPUTER SCIENCE

Convegno

Convegno	11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018
Città	Washington, DC, USA
Periodo	10/7/18 → 13/7/18

Keywords

Complex networks
Computer Science (all)
Machine learning
Prediction
Terrorism
Theoretical Computer Science

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10.1007/978-3-319-93372-6_38

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@inproceedings{e8b701c6b2a24bf997384e877d965db7,

title = "Complex networks for terrorist target prediction",

abstract = "Developments in statistics and computer science have influenced research on many social problems. This process also applies to the study of terrorism. In this context, network analysis is one of the most popular mathematical methods for analyzing terrorist organizations and dynamics. Nonetheless, few studies have applied network science to the analysis of terrorist events. Therefore, in this work we first introduce a novel method to analyze the heterogeneous dynamics of terrorist attacks through the creation of a dynamic meta-network of terror for the period 1997–2016. Second, we use our terrorist meta-network to test the power of Network-based Inference algorithm in predicting terrorist targets. Results are promising and show how this algorithm reaches high levels of precision, accuracy, and recall and indicate that network outcomes can be used in broader machine learning models.",

keywords = "Complex networks, Computer Science (all), Machine learning, Prediction, Terrorism, Theoretical Computer Science, Complex networks, Computer Science (all), Machine learning, Prediction, Terrorism, Theoretical Computer Science",

author = "Campedelli, {Gian Maria} and Iain Cruickshank and Carley, {Kathleen M.}",

year = "2018",

doi = "10.1007/978-3-319-93372-6_38",

language = "English",

isbn = "9783319933719",

volume = "10899",

series = "LECTURE NOTES IN COMPUTER SCIENCE",

pages = "348--353",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

note = "11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018 ; Conference date: 10-07-2018 Through 13-07-2018",

}

Campedelli, GM, Cruickshank, I & Carley, KM 2018, Complex networks for terrorist target prediction. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 10899, LECTURE NOTES IN COMPUTER SCIENCE, pagg. 348-353, 11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018, Washington, DC, USA, 10/7/18. https://doi.org/10.1007/978-3-319-93372-6_38

Complex networks for terrorist target prediction. / Campedelli, Gian Maria; Cruickshank, Iain; Carley, Kathleen M.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10899 2018. pag. 348-353 (LECTURE NOTES IN COMPUTER SCIENCE).

Risultato della ricerca: Contributo in libro › Contributo a convegno

TY - GEN

T1 - Complex networks for terrorist target prediction

AU - Campedelli, Gian Maria

AU - Cruickshank, Iain

AU - Carley, Kathleen M.

PY - 2018

Y1 - 2018

N2 - Developments in statistics and computer science have influenced research on many social problems. This process also applies to the study of terrorism. In this context, network analysis is one of the most popular mathematical methods for analyzing terrorist organizations and dynamics. Nonetheless, few studies have applied network science to the analysis of terrorist events. Therefore, in this work we first introduce a novel method to analyze the heterogeneous dynamics of terrorist attacks through the creation of a dynamic meta-network of terror for the period 1997–2016. Second, we use our terrorist meta-network to test the power of Network-based Inference algorithm in predicting terrorist targets. Results are promising and show how this algorithm reaches high levels of precision, accuracy, and recall and indicate that network outcomes can be used in broader machine learning models.

AB - Developments in statistics and computer science have influenced research on many social problems. This process also applies to the study of terrorism. In this context, network analysis is one of the most popular mathematical methods for analyzing terrorist organizations and dynamics. Nonetheless, few studies have applied network science to the analysis of terrorist events. Therefore, in this work we first introduce a novel method to analyze the heterogeneous dynamics of terrorist attacks through the creation of a dynamic meta-network of terror for the period 1997–2016. Second, we use our terrorist meta-network to test the power of Network-based Inference algorithm in predicting terrorist targets. Results are promising and show how this algorithm reaches high levels of precision, accuracy, and recall and indicate that network outcomes can be used in broader machine learning models.

KW - Complex networks

KW - Computer Science (all)

KW - Machine learning

KW - Prediction

KW - Terrorism

KW - Theoretical Computer Science

KW - Complex networks

KW - Computer Science (all)

KW - Machine learning

KW - Prediction

KW - Terrorism

KW - Theoretical Computer Science

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UR - https://www.springer.com/series/558

U2 - 10.1007/978-3-319-93372-6_38

DO - 10.1007/978-3-319-93372-6_38

M3 - Conference contribution

SN - 9783319933719

VL - 10899

T3 - LECTURE NOTES IN COMPUTER SCIENCE

SP - 348

EP - 353

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018

Y2 - 10 July 2018 through 13 July 2018

ER -

Complex networks for terrorist target prediction

Abstract

Serie di pubblicazioni

Convegno

Keywords

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