2,879 research outputs found
Measuring cascade effects in interdependent networks by using effective graph resistance
Understanding the correlation between the underlie
network structure and overlay cascade effects in the interdependent
networks is one of major challenges in complex network
studies. There are some existing metrics that can be used
to measure the cascades. However, different metrics such as
average node degree interpret different characteristic of network
topological structure, especially less metrics have been identified
to effectively measure the cascading performance in interdependent
networks. In this paper, we propose to use a combined
Laplacian matrix to model the interdependent networks and their
interconnectivity, and then use its effective resistance metric as an
indicator to its cascading behavior. Moreover, we have conducted
extensive comparative studies among different metrics such as
average node degree, and the proposed effective resistance. We
have found that the effective resistance metric can describe more
accurate and finer characteristics on topological structure of
the interdependent networks than average node degree which
is widely adapted by the existing research studies for measuring
the cascading performance in interdependent networks
Using interdependency matrices to mitigate targeted attacks on interdependent networks: A case study involving a power grid and backbone telecommunications networks
Analysis of the interdependencies between interconnected critical infrastructures can help enhance the robustness of the individual infrastructures as well as the overall interconnected infrastructures. One of the most studied interdependent critical infrastructure network scenarios is a power grid connected to a backbone telecommunications network. In this interdependent infrastructure scenario, the robustness of the entire system is usually analyzed in the context of cascading failure models in the power grid. However, this paper focuses on targeted attacks, where an attack on a telecommunications network node directly affects a connected power grid node, and vice versa. Cascading failures are outside the scope of this paper because the objective is to enhance the robustness of the interconnections between the infrastructures. In order to mitigate the impacts of targeted attacks on the interdependent infrastructures, three interdependency matrices for connecting the infrastructures are specified and analyzed. The analysis identifies the interdependency matrix that best reduces the impacts of targeted attacks and the propagation of failures between the infrastructures. Additionally, the impacts of interconnecting a power grid to different telecommunications networks, each with different susceptibilities to targeted attacks, is evaluate
A Critical Review of Robustness in Power Grids using Complex Networks Concepts
Complex network theory for analyzing robustness in energy gridsThis paper reviews the most relevant works that have investigated robustness in power grids using Complex Networks (CN) concepts. In this broad field there are two different approaches. The first one is based solely on topological concepts, and uses metrics such as mean path length, clustering coefficient, efficiency and betweenness centrality, among many others. The second, hybrid approach consists of introducing (into the CN framework) some concepts from Electrical Engineering (EE) in the effort of enhancing the topological approach, and uses novel, more efficient electrical metrics such as electrical betweenness, net-ability, and others. There is however a controversy about whether these approaches are able to provide insights into all aspects of real power grids. The CN community argues that the topological approach does not aim to focus on the detailed operation, but to discover the unexpected emergence of collective behavior, while part of the EE community asserts that this leads to an excessive simplification. Beyond this open debate it seems to be no predominant structure (scale-free, small-world) in high-voltage transmission power grids, the vast majority of power grids studied so far. Most of them have in common that they are vulnerable to targeted attacks on the most connected nodes and robust to random failure. In this respect there are only a few works that propose strategies to improve robustness such as intentional islanding, restricted link addition, microgrids and smart grids, for which novel studies suggest that small-world networks seem to be the best topology.This work has been partially supported by the project TIN2014-54583-C2-2-R from the Spanish Ministerial Commission of Science and Technology (MICYT), by the project S2013/ICE-2933 from Comunidad de Madrid and by the project FUTURE GRIDS-2020 from the Basque Government
Complex Networks from Classical to Quantum
Recent progress in applying complex network theory to problems in quantum
information has resulted in a beneficial crossover. Complex network methods
have successfully been applied to transport and entanglement models while
information physics is setting the stage for a theory of complex systems with
quantum information-inspired methods. Novel quantum induced effects have been
predicted in random graphs---where edges represent entangled links---and
quantum computer algorithms have been proposed to offer enhancement for several
network problems. Here we review the results at the cutting edge, pinpointing
the similarities and the differences found at the intersection of these two
fields.Comment: 12 pages, 4 figures, REVTeX 4-1, accepted versio
Network resilience
Many systems on our planet are known to shift abruptly and irreversibly from
one state to another when they are forced across a "tipping point," such as
mass extinctions in ecological networks, cascading failures in infrastructure
systems, and social convention changes in human and animal networks. Such a
regime shift demonstrates a system's resilience that characterizes the ability
of a system to adjust its activity to retain its basic functionality in the
face of internal disturbances or external environmental changes. In the past 50
years, attention was almost exclusively given to low dimensional systems and
calibration of their resilience functions and indicators of early warning
signals without considerations for the interactions between the components.
Only in recent years, taking advantages of the network theory and lavish real
data sets, network scientists have directed their interest to the real-world
complex networked multidimensional systems and their resilience function and
early warning indicators. This report is devoted to a comprehensive review of
resilience function and regime shift of complex systems in different domains,
such as ecology, biology, social systems and infrastructure. We cover the
related research about empirical observations, experimental studies,
mathematical modeling, and theoretical analysis. We also discuss some ambiguous
definitions, such as robustness, resilience, and stability.Comment: Review chapter
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
Cascading Failures in Complex Networks
Cascading failure is a potentially devastating process that spreads on
real-world complex networks and can impact the integrity of wide-ranging
infrastructures, natural systems, and societal cohesiveness. One of the
essential features that create complex network vulnerability to failure
propagation is the dependency among their components, exposing entire systems
to significant risks from destabilizing hazards such as human attacks, natural
disasters or internal breakdowns. Developing realistic models for cascading
failures as well as strategies to halt and mitigate the failure propagation can
point to new approaches to restoring and strengthening real-world networks. In
this review, we summarize recent progress on models developed based on physics
and complex network science to understand the mechanisms, dynamics and overall
impact of cascading failures. We present models for cascading failures in
single networks and interdependent networks and explain how different dynamic
propagation mechanisms can lead to an abrupt collapse and a rich dynamic
behavior. Finally, we close the review with novel emerging strategies for
containing cascades of failures and discuss open questions that remain to be
addressed.Comment: This review has been accepted for publication in the Journal of
Complex Networks Published by Oxford University Pres
Network science based quantification of resilience demonstrated on the Indian Railways Network
The structure, interdependence, and fragility of systems ranging from power
grids and transportation to ecology, climate, biology and even human
communities and the Internet, have been examined through network science. While
the response to perturbations has been quantified, recovery strategies for
perturbed networks have usually been either discussed conceptually or through
anecdotal case studies. Here we develop a network science-based quantitative
methods framework for measuring, comparing and interpreting hazard responses
and as well as recovery strategies. The framework, motivated by the recently
proposed temporal resilience paradigm, is demonstrated with the Indian Railways
Network. The methods are demonstrated through the resilience of the network to
natural or human-induced hazards and electric grid failure. Simulations
inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as
well as a cyber-physical attack scenario. Multiple metrics are used to generate
various recovery strategies, which are simply sequences in which system
components should be recovered after a disruption. Quantitative evaluation of
recovery strategies suggests that faster and more resource-effective recovery
is possible through network centrality measures. Case studies based on two
historical events, specifically the 2004 Indian Ocean tsunami and the 2012
North Indian blackout, and a simulated cyber-physical attack scenario, provides
means for interpreting the relative performance of various recovery strategies.
Quantitative evaluation of recovery strategies suggests that faster and more
resource-effective restoration is possible through network centrality measures,
even though the specific strategy may be different for sub-networks or for the
partial recovery
Optimizing topological cascade resilience based on the structure of terrorist networks
Complex socioeconomic networks such as information, finance and even
terrorist networks need resilience to cascades - to prevent the failure of a
single node from causing a far-reaching domino effect. We show that terrorist
and guerrilla networks are uniquely cascade-resilient while maintaining high
efficiency, but they become more vulnerable beyond a certain threshold. We also
introduce an optimization method for constructing networks with high passive
cascade resilience. The optimal networks are found to be based on cells, where
each cell has a star topology. Counterintuitively, we find that there are
conditions where networks should not be modified to stop cascades because doing
so would come at a disproportionate loss of efficiency. Implementation of these
findings can lead to more cascade-resilient networks in many diverse areas.Comment: 26 pages. v2: In review at Public Library of Science ON
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