___

• Chatfield AT, Reddick CG, Brajawidagda U. Tweeting propaganda, radicalization and recruitment: Islamic State supporters multi-sided twitter networks. In: ACM 16th Annual International Conference on Digital Government Research; 27–30 May 2015; Phoenix, AZ, USA. New York, NY, USA: ACM. pp. 239-249.
• Carrington PJ. Crime and social network analysis. In: Scott J, Carrington PJ, editors. The SAGE Handbook of Social Network Analysis. London, UK: SAGE Publications, 2011. pp. 236-255.
• Calderoni F. Social network analysis of organized criminal groups. In: Bruinsma G, Weisburd D, editors. Encyclo- pedia of Criminology and Criminal Justice. New York, NY, USA: Springer, 2014. pp. 4972-4981.
• Helbing D, Brockmann D, Chadefaux T, Donnay K, Blanke U, Woolley-Meza O, Moussaid M, Johansson A, Krause J, Schutte S et al. Saving human lives: what complexity science and information systems can contribute. J Stat Phys 2015; 158: 735-781.
• Goodarzinick A, Niry MD, Valizadeh A, Perc M. Robustness of functional networks at criticality against structural defects. Phys Rev E 2018; 98: 022312.
• Jalili M, Perc M. Information cascades in complex networks. J Complex Netw 2017; 5: 665–693.
• Krebs V. Uncloaking terrorist networks. First Monday 2002; 7: 4.
• Husslage B, Borm P, Burg T, Hamers H, Lindelauf R. Ranking terrorists in networks: a sensitivity analysis of Al Qaeda’s 9/11 attack. Soc Networks 2015; 42: 1-7.
• Morselli, C. Inside Criminal Networks. 8th ed. New York, NY, USA: Springer, 2009.
• Gutfraind A, Genkin M. A graph database framework for covert network analysis: an application to the Islamic State network in Europe. Soc Networks 2017; 51: 178-188.
• Natarajan M. Understanding the structure of a large heroin distribution network: a quantitative analysis of qualitative data. J Quant Criminol 2006; 22: 171-192.
• Lupsha PA. Networks versus networking: analysis of an organized crime group. In: Waldo GP, editor. Career Criminals. Beverly Hills, CA, USA: SAGE Publications, 1983. pp. 59-87.
• Davis RH. Social network analysis: an aid in conspiracy investigations. FBI Law Enforcement Bulletin 1981; 50: 11-19.
• Sparrow MK. The application of network analysis to criminal intelligence: an assessment of the prospects. Soc Networks 1991; 13: 251-274.
• Berlusconi G. Social network analysis and crime prevention. In: Leclerc B, Savona E, editors. Crime Prevention in the 21st Century. Cham, Switzerland: Springer, 2017. pp. 129-141.
• D’Orsogna MR, Perc M. Statistical physics of crime: a review. Phys Life Rev 2015; 12: 1-21.
• Shang X, Yuan Y. Social network analysis in multiple social networks data for criminal group discovery. In: IEEE International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC); 10–12 October 2012; Sanya, China. New York, NY, USA: IEEE. pp. 27-30.
• Borgatti SP, Everett MG, Freeman LC. Ucinet. In: Alhajj R, Rokne J. editors. Encyclopedia of Social Network Analysis and Mining. New York, NY, USA: Springer, 2014. pp. 2261-2267.
• Zachary WW. An information flow model for conflict and fission in small groups. J Anthropol Res 1977; 33: 452-473.
• Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. Network motifs: simple building blocks of complex networks. Science 2002; 298: 824-827.
• Kashtan N, Itzkovitz S, Milo R, Alon U. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 2004; 20: 1746-1758.
• Wernicke S, Rasche F. FANMOD: a tool for fast network motif detection. Bioinformatics 2006; 22: 1152-1153.
• Kashani ZRM, Ahrabian H, Elahi E, Nowzari-Dalini A, Ansari ES, Asadi S, Masoudi-Nejad A. Kavosh: a new algorithm for finding network motifs. BMC Bioinformatics 2009; 10: 318.
• Chen J, Hsu W, Lee ML, Ng SK. NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs. In: ACM 12th SIGKDD International Conference on Knowledge Discovery and Data Mining; 20–23 August 2006; Philadelphia, PA, USA. New York, NY, USA: ACM. pp. 106-115.
• Omidi S, Schreiber F, Masoudi-Nejad A. MODA: an efficient algorithm for network motif discovery in biological networks. Genes Genet Syst 2009; 84: 385-395.
• Grochow JA, Kellis M. Network motif discovery using subgraph enumeration and symmetry-breaking. In: 11th Annual International Conference on Research in Computational Molecular Biology; 21–25 April 2007; Oakland, CA, USA. Berlin, Germany: Springer. pp. 92-106.
• Schreiber F, Schwöbbermeyer H. Frequency concepts and pattern detection for the analysis of motifs in networks. Transactions on Computational Systems Biology III 2005; 3: 89-104
• Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, Ayzenshtat I, Alon U. Superfamilies of evolved and designed networks. Science 2004. 303: 1538-1542.
• Xiong H, Capurso D, Sen Ś, Segal MR. Sequence-based classification using discriminatory motif feature selection. PLoS One 2011; 6: e27382.
• Buza K, Schmidt-Thieme L. Motif-based classification of time series with bayesian networks and svms. In: 32nd Annual Conference of the Gesellschaft für Klassifikation (GfKI 2008); 16–18 July 2008; Hamburg, Germany. Berlin, Germany: Springer. pp. 105-114.
• Kunik V, Solan Z, Edelman S, Ruppin E, Horn D. Motif extraction and protein classification.In: Computational Systems Bioinformatics Conference; 8–11 August 2005; Stanford, CA, USA. New York, NY, USA: IEEE. pp. 80-85.
• Xing EP, Karp RM. MotifPrototyper: a Bayesian profile model for motif families. P Natl Acad Sci USA 2004; 101: 10523-10528.
• Leuprecht C, Hall K. Why terror networks are dissimilar: how structure relates to function. In: Masys AJ, editor. Networks and Network Analysis for Defence and Security. Cham, Switzerland: Springer, 2014. pp. 83-120.