152,134 research outputs found
Ranking in evolving complex networks
Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google’s PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes
Laplacian paths in complex networks: Information core emerges from entropic transitions
Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information
pathways are expected to pervade these scales reflecting structural insights that are not manifest from analyses
of the network topology. Moreover, small-world effects correlate with the different network hierarchies complicating
the identification of coexisting mesoscopic structures and functional cores.We present a communicability
analysis of effective information pathways throughout complex networks based on information diffusion to shed
further light on these issues. We employ a variety of brand-new theoretical techniques allowing for: (i) bring
the theoretical framework to quantify the probability of information diffusion among nodes, (ii) identify critical
scales and structures of complex networks regardless of their intrinsic properties, and (iii) demonstrate their
dynamical relevance in synchronization phenomena. By combining these ideas, we evidence how the information
flow on complex networks unravels different resolution scales. Using computational techniques, we focus on
entropic transitions, uncovering a generic mesoscale object, the information core, and controlling information
processing in complex networks. Altogether, this study sheds much light on allowing new theoretical techniques
paving the way to introduce future renormalization group approaches based on diffusion distances
Canalization and control in automata networks: body segmentation in Drosophila melanogaster
We present schema redescription as a methodology to characterize canalization
in automata networks used to model biochemical regulation and signalling. In
our formulation, canalization becomes synonymous with redundancy present in the
logic of automata. This results in straightforward measures to quantify
canalization in an automaton (micro-level), which is in turn integrated into a
highly scalable framework to characterize the collective dynamics of
large-scale automata networks (macro-level). This way, our approach provides a
method to link micro- to macro-level dynamics -- a crux of complexity. Several
new results ensue from this methodology: uncovering of dynamical modularity
(modules in the dynamics rather than in the structure of networks),
identification of minimal conditions and critical nodes to control the
convergence to attractors, simulation of dynamical behaviour from incomplete
information about initial conditions, and measures of macro-level canalization
and robustness to perturbations. We exemplify our methodology with a well-known
model of the intra- and inter cellular genetic regulation of body segmentation
in Drosophila melanogaster. We use this model to show that our analysis does
not contradict any previous findings. But we also obtain new knowledge about
its behaviour: a better understanding of the size of its wild-type attractor
basin (larger than previously thought), the identification of novel minimal
conditions and critical nodes that control wild-type behaviour, and the
resilience of these to stochastic interventions. Our methodology is applicable
to any complex network that can be modelled using automata, but we focus on
biochemical regulation and signalling, towards a better understanding of the
(decentralized) control that orchestrates cellular activity -- with the
ultimate goal of explaining how do cells and tissues 'compute'
Deciphering the imprint of topology on nonlinear dynamical network stability
Coupled oscillator networks show complex interrelations between topological characteristics of the network and the nonlinear stability of single nodes with respect to large but realistic perturbations. We extend previous results on these relations by incorporating sampling-based measures of the transient behaviour of the system, its survivability, as well as its asymptotic behaviour, its basin stability. By combining basin stability and survivability we uncover novel, previously unknown asymptotic states with solitary, desynchronized oscillators which are rotating with a frequency different from their natural one. They occur almost exclusively after perturbations at nodes with specific topological properties. More generally we confirm and significantly refine the results on the distinguished role tree-shaped appendices play for nonlinear stability. We find a topological classification scheme for nodes located in such appendices, that exactly separates them according to their stability properties, thus establishing a strong link between topology and dynamics. Hence, the results can be used for the identification of vulnerable nodes in power grids or other coupled oscillator networks. From this classification we can derive general design principles for resilient power grids. We find that striving for homogeneous network topologies facilitates a better performance in terms of nonlinear dynamical network stability. While the employed second-order Kuramoto-like model is parametrised to be representative for power grids, we expect these insights to transfer to other critical infrastructure systems or complex network dynamics appearing in various other fields.Bundesministerium fĂĽr Bildung und Forschung https://doi.org/10.13039/501100002347Peer Reviewe
Network centrality: an introduction
Centrality is a key property of complex networks that influences the behavior
of dynamical processes, like synchronization and epidemic spreading, and can
bring important information about the organization of complex systems, like our
brain and society. There are many metrics to quantify the node centrality in
networks. Here, we review the main centrality measures and discuss their main
features and limitations. The influence of network centrality on epidemic
spreading and synchronization is also pointed out in this chapter. Moreover, we
present the application of centrality measures to understand the function of
complex systems, including biological and cortical networks. Finally, we
discuss some perspectives and challenges to generalize centrality measures for
multilayer and temporal networks.Comment: Book Chapter in "From nonlinear dynamics to complex systems: A
Mathematical modeling approach" by Springe
The failure tolerance of mechatronic software systems to random and targeted attacks
This paper describes a complex networks approach to study the failure
tolerance of mechatronic software systems under various types of hardware
and/or software failures. We produce synthetic system architectures based on
evidence of modular and hierarchical modular product architectures and known
motifs for the interconnection of physical components to software. The system
architectures are then subject to various forms of attack. The attacks simulate
failure of critical hardware or software. Four types of attack are
investigated: degree centrality, betweenness centrality, closeness centrality
and random attack. Failure tolerance of the system is measured by a 'robustness
coefficient', a topological 'size' metric of the connectedness of the attacked
network. We find that the betweenness centrality attack results in the most
significant reduction in the robustness coefficient, confirming betweenness
centrality, rather than the number of connections (i.e. degree), as the most
conservative metric of component importance. A counter-intuitive finding is
that "designed" system architectures, including a bus, ring, and star
architecture, are not significantly more failure-tolerant than interconnections
with no prescribed architecture, that is, a random architecture. Our research
provides a data-driven approach to engineer the architecture of mechatronic
software systems for failure tolerance.Comment: Proceedings of the 2013 ASME International Design Engineering
Technical Conferences & Computers and Information in Engineering Conference
IDETC/CIE 2013 August 4-7, 2013, Portland, Oregon, USA (In Print
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
- …