TY - JOUR
T1 - Temporal dynamics in covert networks
AU - Broccatelli, C.
AU - Everett, M.
AU - Koskinen, J.
PY - 2016
Y1 - 2016
N2 - Criminal organisations tend to be structured as a network in order to optimise flows of resource exchanges. The consolidation of these structures strengthens the criminal organisation’s capability of becoming more and more efficient over time. Recently, researchers in the field of covert networks have begun to analyse the dynamics of these networks. Thus far, the models and methods used to analyse the temporal dynamics of covert networks have come with a number of limitations. Our approach for analysing temporal dynamics attempts to address some of these limitations. In this article, we extend the use of dynamic line-graphs to bipartite networks for incorporating time directly into the network, and we suggest an alternative way to visualise the evolution of actors’ participation in successive covert actions and events. Our article intends to contribute to the research on dynamic networks by proposing a new approach for representing temporal dynamics in covert networks. After illustrating our method, we present some examples of its use on real-world data for visualising network evolutions over time.
AB - Criminal organisations tend to be structured as a network in order to optimise flows of resource exchanges. The consolidation of these structures strengthens the criminal organisation’s capability of becoming more and more efficient over time. Recently, researchers in the field of covert networks have begun to analyse the dynamics of these networks. Thus far, the models and methods used to analyse the temporal dynamics of covert networks have come with a number of limitations. Our approach for analysing temporal dynamics attempts to address some of these limitations. In this article, we extend the use of dynamic line-graphs to bipartite networks for incorporating time directly into the network, and we suggest an alternative way to visualise the evolution of actors’ participation in successive covert actions and events. Our article intends to contribute to the research on dynamic networks by proposing a new approach for representing temporal dynamics in covert networks. After illustrating our method, we present some examples of its use on real-world data for visualising network evolutions over time.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85042758449&partnerID=MN8TOARS
UR - https://www.scopus.com/pages/publications/85042758449
U2 - 10.1177/2059799115622766
DO - 10.1177/2059799115622766
M3 - Article
SN - 2059-7991
VL - 9
SP - 1
EP - 14
JO - Methodological Innovations
JF - Methodological Innovations
ER -