scholarly journals Depth Penetration and Scope Extension of Failures in the Cascading of Multilayer Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wen-Jun Jiang ◽  
Run-Ran Liu ◽  
Chun-Xiao Jia
Keyword(s):  
Real World ◽  
Propagation Direction ◽  
Intervention Programs ◽  
Theoretical Studies ◽  
Percolation Transition ◽  
Multilayer Networks ◽  
Microscopic Mechanism ◽  
Network Layers ◽  
Avalanche Process ◽  
Random Failures

Real-world complex systems always interact with each other, which causes these systems to collapse in an avalanche or cascading manner in the case of random failures or malicious attacks. The robustness of multilayer networks has attracted great interest, where the modeling and theoretical studies of which always rely on the concept of multilayer networks and percolation methods. A straightforward and tacit assumption is that the interdependence across network layers is strong, which means that a node will fail entirely with the removal of all links if one of its interdependent nodes in other network layers fails. However, this oversimplification cannot describe the general form of interactions across the network layers in a real-world multilayer system. In this paper, we reveal the nature of the avalanche disintegration of general multilayer networks with arbitrary interdependency strength across network layers. Specifically, we identify that the avalanche process of the whole system can essentially be decomposed into two microscopic cascading dynamics in terms of the propagation direction of the failures: depth penetration and scope extension. In the process of depth penetration, the failures propagate from layer to layer, where the greater the number of failed nodes is, the greater is the destructive power that will emerge in an interdependency group. In the process of scope extension, failures propagate with the removal of connections in each network layer. Under the synergy of the two processes, we find that the percolation transition of the system can be discontinuous or continuous with changes in the interdependency strength across network layers, which means that a sudden system-wide collapse can be avoided by controlling the interdependency strength across network layers. Our work not only reveals the microscopic mechanism of global collapse in multilayer infrastructure systems but also provides stimulating ideas on intervention programs and approaches for cascade failures.

Strengthening and Enriching Audit Practice

2012 ◽  
pp. 97-108
Author(s):  
Peter A. C. Smith
Keyword(s):  
Real World ◽  
Theory And Practice ◽  
Network Visualization ◽  
Viable System Model ◽  
Practical Applications ◽  
Visualization Analysis ◽  
The Past ◽  
Network Layers ◽  
The Face ◽  
Viable System

The audit profession has been facing reassessment and repositioning for the past decade. Enquiry has been an integral part of an audit; however, its reliability as a source of audit evidence is questioned. To legitimize enquiry in the face of audit complexity and ensure sufficiency, relevance, and reliability, the introduction of Stafford Beer’s Viable System Model (VSM) into theory and practice has been recommended by a number of authors. In this paper, a variant on previous VSM-based audit work is introduced to perfect auditing assessment of accountability and compliance. This variant is termed the “VSM/NVA variant” and is applicable when the VSM model is in use for an audit. This variant is based on application of Network Visualization Analysis (NVA) to a VSM-modeled organization. Using NVA, “decision leaders” can be identified and their socio-technical relevance to VSM systems explored. This paper shows how the concepts of decision leaders and their networks can enrich and clarify practical applications of audit theory and practice. The approach provides an enhanced real-world understanding of how various VSM systems and network layers of an organization coalesce, and how they relate to the aims of the VSM model at micro and macro levels.

scholarly journals Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Massimiliano Zanin ◽  
Xiaoqian Sun ◽  
Sebastian Wandelt
Keyword(s):  
Complex Network ◽  
Real World ◽  
Paradigm Shift ◽  
Transportation Networks ◽  
Transportation Systems ◽  
Node Ranking ◽  
Targeted Attacks ◽  
Topological Characteristics ◽  
Definition Of ◽  
Random Failures

The introduction of complex network concepts in the study of transportation systems has supposed a paradigm shift and has allowed understanding different transport phenomena as the emergent result of the interactions between the elements composing them. In spite of several notable achievements, lurking pitfalls are undermining our understanding of the topological characteristics of transportation systems. In this study, we analyse four of the most common ones, specifically related to the assessment of the scale-freeness of networks, the interpretation and comparison of topological metrics, the definition of a node ranking, and the analysis of the resilience against random failures and targeted attacks. For each topic we present the problem from both a theoretical and operational perspective, for then reviewing how it has been tackled in the literature and finally proposing a set of solutions. We further use six real-world transportation networks as case studies and discuss the implications of these four pitfalls in their analysis. We present some future lines of work that are stemming from these pitfalls and that will allow a deeper understanding of transportation systems from a complex network perspective.

scholarly journals Topological clustering of multilayer networks

2021 ◽  
Vol 118 (21) ◽  
pp. e2019994118
Author(s):  
Monisha Yuvaraj ◽  
Asim K. Dey ◽  
Vyacheslav Lyubchich ◽  
Yulia R. Gel ◽  
H. Vincent Poor
Keyword(s):  
Brain Regions ◽  
Higher Order ◽  
Topological Data Analysis ◽  
Critical Infrastructures ◽  
Network Clustering ◽  
Multilayer Networks ◽  
Network Layers ◽  
Brain Connectome ◽  
High Utility ◽  
Persistence Diagrams

Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.

scholarly journals Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Quang Nguyen ◽  
Tuan V. Vu ◽  
Hanh-Duyen Dinh ◽  
Davide Cassi ◽  
Francesco Scotognella ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Real World ◽  
Ad Hoc ◽  
Modular Structure ◽  
Network Robustness ◽  
Node Degree ◽  
Percolation Transition ◽  
Scale Free ◽  
Model Networks

AbstractIn this paper we investigate how the modularity of model and real-world social networks affect their robustness and the efficacy of node attack (removal) strategies based on node degree (ID) and node betweenness (IB). We build Barabasi–Albert model networks with different modularity by a new ad hoc algorithm that rewire links forming networks with community structure. We traced the network robustness using the largest connected component (LCC). We find that when model networks present absent or low modular structure ID strategy is more effective than IB to decrease the LCC. Conversely, in the case the model network present higher modularity, the IB strategy becomes the most effective to fragment the LCC. In addition, networks with higher modularity present a signature of a 1st order percolation transition and a decrease of the LCC with one or several abrupt changes when nodes are removed, for both strategies; differently, networks with non-modular structure or low modularity show a 2nd order percolation transition networks when nodes are removed. Last, we investigated how the modularity of the network structure evaluated by the modularity indicator (Q) affect the network robustness and the efficacy of the attack strategies in 12 real-world social networks. We found that the modularity Q is negatively correlated with the robustness of the real-world social networks for both the node attack strategies, especially for the IB strategy (p-value < 0.001). This result indicates how real-world networks with higher modularity (i.e. with higher community structure) may be more fragile to node attack. The results presented in this paper unveil the role of modularity and community structure for the robustness of networks and may be useful to select the best node attack strategies in network.

scholarly journals Layer entanglement in multiplex, temporal multiplex, and coupled multilayer networks

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Blaž Škrlj ◽  
Benjamin Renoust
Keyword(s):  
Real World ◽  
Biological Networks ◽  
Real Life ◽  
Network Activity ◽  
Dependent Data ◽  
World Systems ◽  
Multilayer Networks ◽  
Single Model ◽  
Elementary Layer ◽  
Random Generator

Abstract Complex networks, such as transportation networks, social networks, or biological networks, capture the complex system they model by often representing only one type of interactions. In real world systems, there may be many different aspects that connect entities together. These can be captured using multilayer networks, which combine different modalities of interactions in a single model. Coupling in multilayer networks may exhibit different properties which can be related to the very nature of the data they model (or to events in time-dependent data). We hypothesise that such properties may be reflected in the way layers are intertwined. In this paper, we investigated these through the prism of layer entanglement in coupled multilayer networks. We test over 30 real-life networks in 6 different disciplines (social, genetic, transport, co-authorship, trade, and neuronal networks). We further propose a random generator, displaying comparable patterns of elementary layer entanglement and transition coupling entanglement across 1,329,696 synthetic coupled multilayer networks. Our experiments demonstrate difference of layer entanglement across disciplines, and even suggest a link between entanglement intensity and homophily. We additionally study entanglement in 3 real world temporal datasets displaying a potential rise in entanglement activity prior to other network activity.

Link prediction for multilayer networks using interlayer structural information

2021 ◽  
Author(s):  
Fengqin Tang ◽  
Chunning Wang ◽  
Yuanyuan Wang ◽  
Jinxia Su
Keyword(s):  
Complex Systems ◽  
Real World ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Structural Information ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Type Form ◽  
Target Layer

A multilayer network is a useful representation for real-world complex systems in which multiple types of connections are formed between entities. Connections of the same type form a specific layer of the network. We propose a novel framework for predicting links in a target layer of a multilayer network by taking into account the interlayer structural information. The method depends on the intuitive assumption that two node pairs in the target layer tend to have similar connection patterns if these pairs of nodes are similar. Further, the prediction accuracy will be improved in the target layer if the structural information of the copies of the node pairs in relevant layers is employed. We demonstrate the effectiveness of the proposed method experimentally by applying it to both simulated and real-world multilayer networks.

scholarly journals A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Hyobin Kim ◽  
Omar K. Pineda ◽  
Carlos Gershenson
Keyword(s):  
Complex Systems ◽  
Real World ◽  
Multilayer Structure ◽  
Boolean Network ◽  
Single Layer ◽  
Network Models ◽  
Boolean Networks ◽  
Multilayer Structures ◽  
Multilayer Networks ◽  
Engineered Systems

Antifragility is a property from which systems are able to resist stress and furthermore benefit from it. Even though antifragile dynamics is found in various real-world complex systems where multiple subsystems interact with each other, the attribute has not been quantitatively explored yet in those complex systems which can be regarded as multilayer networks. Here we study how the multilayer structure affects the antifragility of the whole system. By comparing single-layer and multilayer Boolean networks based on our recently proposed antifragility measure, we found that the multilayer structure facilitated the production of antifragile systems. Our measure and findings will be useful for various applications such as exploring properties of biological systems with multilayer structures and creating more antifragile engineered systems.

Replications can cause distorted belief in scientific progress

2018 ◽  
Vol 41 ◽  
Author(s):  
Michał Białek
Keyword(s):  
Real World ◽  
Scientific Progress ◽  
The Public ◽  
Psychological Science

AbstractIf we want psychological science to have a meaningful real-world impact, it has to be trusted by the public. Scientific progress is noisy; accordingly, replications sometimes fail even for true findings. We need to communicate the acceptability of uncertainty to the public and our peers, to prevent psychology from being perceived as having nothing to say about reality.

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