Therefore, enhancing robustness of networks in such a way that it is not dependent on attack strategies is very valuable and essential. Networks utilizing the soft reset mechanism displayed the smallest range of PD50 values. It derives from the Latin robustus , meaning "strength." Neural networks provide state-of-the-art results for most machine learning tasks. Specific choices of temporal granularity, number of snapshots and observation duration … II. Currently, there is no consensus about the definition of robustness in complex networks; however, there are some works that analyze these concepts [11–14]. • A numerical measure has been designed for guiding the optimization process. Then in section V wegivetwoexamplesillustrating important applications of the small gain theorem to robust stability analysis and design. • The performance of the proposed algorithm outperforms existing optimization approaches. Global Robustness Verification Networks. Our daily routines would cease to exist should the technological information infrastructure disintegrate. Funding. The study of robustness in complex networks is important for many fields. Next, in Chapter 3, we will return to the world of deep networks, and look at the inner maximization problem, focusing on the three general classes of approaches that can be applied: 1) lower bounds (i.e., constructing the adversarial example), 2) exact solutions (via combinatorial optimization), 3) upper bounds (usually with some more-tractable strategy). Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. ∙ Peking University ∙ 0 ∙ share . In most studies, different types of networks (such as food webs, parasitoid webs, seed dispersal networks, and pollination networks) have been studied separately. For a wide class of chemical reaction networks, including all those governed by detailed balanced mass-action kinetics, we examine the robustness of equilibrium species concentra-tions against fluctuations in the overall reactant supply. destruction of data to dismantling of entire networks. Robustness, the ability to withstand failures and perturbations, is a critical attribute of many complex systems including complex networks.. • A numerical measure has been designed for guiding the optimization process. A property is {\\em robust} in a given graph if it is inherited by all connected spanning subgraphs of this graph. In particu-lar, we show in the second example how feedback can be used to increase robustness, so that the ef-fect of environmental influences is reduced. Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. Shao et al. Thus, it becomes crucial to maintain the highest levels of robustness in complex networks. Towards Evaluating the Robustness of Neural Networks. SENSITIVITY AND ROBUSTNESS IN CHEMICAL REACTION NETWORKS* GUY SHINARt, URI ALONt, AND MARTIN FEINBERG* Abstract. This work was supported by the Volkswagen Foundation. 08/16/2016 ∙ by Nicholas Carlini, et al. However, the network robustness against cascading failures under various controllable parameters has not been systematically studied.Therefore, based on load-capacity(LC) cascading failures model, a new definition of node load is proposed. Understanding species’ interactions and the robustness of interaction networks to species loss is essential to understand the effects of species’ declines and extinctions. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). Abstract. In this paper, we analyse the spatio-temporal robustness of transport, biological and social systems through six real-world networks. However, most existing works focus on robustness of neural networks on image classification problems, where convolutional neural networks (CNNs) are … Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. However, little is known about the relation between them. Many complex systems, from power grids and the internet, to the brain and society, can be modeled using modular networks. 06/08/2020 ∙ by Weidi Sun, et al. In this report, we investigate dynamical robustness of a complex network to noise injected through one of its nodes. Network robustness is a core problem in network researches, it has achieved many good results by now. Modules, densely interconnected groups of elements, often overlap due to elements that belong to multiple modules. ∙ 0 ∙ share . Social networks often display this sort of time dependence and many neural networks also change through time [34, 35]. robustness arise in biological and social systems, includ-ing questions such as the stability of social organizations in the face of famine, war, or even changes in social pol-icy. We extend the definition of community robustness into interdependent networks. Robustness testing of secure Wireless Sensor Networks January 2014 Conference: ACECS (International Conference on Automation, Control, Engineering and Computer Science) Spartan Networks: Self-feature-squeezing neural networks for increased robustness in adversarial settings. [9] proposed a more general network model where interdependent networks may have multi- Robust ( pronounced RO-buhst ) is an adjective commonly applied in marketing literature to information technology products in several ways. ... the authors propose an alogrithm to generate samples that are missclassified while retaining their meaning to the human cognitive system. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We investigate a special case of hereditary property that we refer to as {\\em robustness}. networks, which are largely decentralized and highly dynamic systems. Here we detail how each network is constructed. To quantify the trends in robustness for each series of networks, the midpoint and rate of performance decay was measured for each curve by fitting a sigmoidal function to it (fits were very good with median R 2 = 99.9%). In real world, it is hard to determine which type of targeted attacks may threat networks functionality. For these networks caution must be taken before applying methods of extracting skeletons or backbones since their changing states will lead to different results. We have found that networks of Chua's circuits and Rossler's systems are representative of two opposite behaviors that can be exhibited by a network in terms of its dynamical robustness to noise. Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. In particular, for any given network size and diameter, we identify networks that are maximally robust and then analyze their strong structural controllability. Our quantitative definition of the “effective fitness” or robustness of a transcription factor network by mutation load (and the lowest eigenvalue of the operator describing the evolution of a population of networks) can be extended to complex networks mapping … Robustness of neural networks has been an active topic of investigation, and a number of approaches have been proposed. The wide deployment of deep neural networks, though achieving great success in many domains, has severe safety and reliability concerns. In fact, it has not guaranteed that a robust network against HBA could perform well against HDA yet. (a) Efficiency E, (b) modularity Q, (c) robustness to failure f F, (d) robustness to attack f A, (e) global clustering coefficient C and (f) mean squared degree d 2 of the connected components of twelve social networks and the connected components of versions of these networks randomly rewired in an inclusive way or in an exclusive way with inclusivity parameter either r = 1 or r = 2. Understanding robustness in Random Boolean Networks Kai Willadsen1,2, Jochen Triesch1 and Janet Wiles2,3 1Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany 2School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia 3ARC Centre for Complex Systems, School of Information … The robustness of such networks can be improved by a small degree of modification [1]. • Some other non-rewiring enhancement strategies have been developed to provide comparisons. ACRE has a user-friendly interface, and it can facilitate efficient analysis of key structural features that guarantee the presence and the absence of the ACR property from combinatorial networks. Almost all network research has been focused on the properties of a single network that does not interact and depends on other networks. In this paper, we study the relationship between two crucial properties in linear dynamical networks of diffusively coupled agents -- controllability and robustness to noise and structural changes in the network. In ecology, robustness is an important attribute of ecosystems, and can give insight into the reaction to disturbances such as the extinction of species. (See Related work below.) One cannot have explicit control over their structure to ensure properties like robustness under various types of disrupting events such as a random failure or an intended attack. Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach • We extend the definition of community robustness into interdependent networks. Because of its broad application, robustness has be-come a central topic in all complex networks and … robustness of two coupled networks, and dependency is represented as a one-to-one correspondence between two networks, meaning that each node in one networkdepends on one and only one node in the other network and vice versa. Here, we introduce a software tool, absolute concentration robustness explorer (ACRE), which efficiently explores combinatorial biochemical networks for the ACR property. for quantum networks are described in section IV.
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