Advances in dynamic temporal networks: Understanding the temporal dynamics of complex adaptive networks

Sloot, Peter M. A.; Kampis, George; Gulyás, László

doi:10.1140/epjst/e2013-01926-8

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…Criminal networks can change for several reasons: as a result of new business opportunities, as a consequence of competition that requires a defensive orientation or as a direct result of law enforcement controls that may lead to the downfall, stagnation or adaptation of the network 7 . The changing effects of network disruption can therefore only be understood within its dynamics, these networks are truly complex adaptive systems 17 . Many researchers of criminal networks agree that “studying … the dynamics in criminal networks is probably the most challenging obstacle facing anyone approaching this area .” 3 7 Although we recognize the complexity and difficulty that is associated with studying change within social networks, we attempt to capture these dynamics within a computational framework.…”

Section: The Complexity Of Criminal Networkmentioning

confidence: 99%

The Relative Ineffectiveness of Criminal Network Disruption

Duijn

Kashirin

Sloot

2014

Sci Rep

Self Cite

187

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Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, data-driven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become ‘stronger’, after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re-)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a long-term effort.

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Section: The Complexity Of Criminal Networkmentioning

confidence: 99%

The Relative Ineffectiveness of Criminal Network Disruption

Duijn

Kashirin

Sloot

2014

Sci Rep

Self Cite

187

160

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“…However, depending on the timescale of the disease's dynamics and the type of contact(s) relevant for its transmission, this may be an unrealistic assumption. Network dynamics can have an important and complex influence on epidemic outcomes, in some cases facilitating and in other cases hindering disease spread depending on a complex interplay of contact turnover rates, transmission and recovery rates, and structural features of the network (Fefferman and Ng, 2007; Vernon and Keeling, 2009; Volz and Meyers, 2009; Taylor et al, 2012; Sloot et al, 2013; Gulyás and Kampis, 2013). …”

Section: Performance Of Link Removal Algorithmsmentioning

confidence: 99%

Link removal for the control of stochastically evolving epidemics over networks: A comparison of approaches

Enns

Brandeau

2015

Journal of Theoretical Biology

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For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two “preventive” approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two “reactive” approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdős-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdős-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing which nodes are initially infected by comparing the performance improvement achieved by reactive over preventive strategies. We find that such information is most valuable for moderate budget levels, with increasing value as disease spread becomes more likely (due to either increased connectedness of the network or increased infectiousness of the disease).

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“…In 2010 the ITMO Faculty of Information Technology and Programming and eScience Institute won a 150 million Ruble (3.5 mln Euro) grant for setting up an Advanced Computing Lab [5] under the leadership of Prof. Sloot [6]. Within this project, researchers worked together on cutting-edge computational science problems in complex networks and dynamical systems [17], [18], in adaptive load balancing for distributed computing [20], in virtual problem-solving environments for semiconductor research [21], [22], in artificial intelligence [23] and finite element models [24] for flood early warning and decision support systems [25], [26]. Another goal of the project was development of joint educational programs.…”

Section: Why University Of Amsterdam and Itmo University?mentioning

confidence: 99%