A preferential attachment strategy for connectivity link addition strategy in improving the robustness of interdependent networks

2017 ◽  
Vol 483 ◽  
pp. 412-422 ◽  
Author(s):  
Xingyuan Wang ◽  
Jianye Cao ◽  
Rui Li ◽  
Tianfang Zhao
Keyword(s):  
Preferential Attachment ◽  
Interdependent Networks ◽  
Attachment Strategy

scholarly journals Preferential attachment in the interaction between dynamically generated interdependent networks

2012 ◽  
Vol 100 (5) ◽  
pp. 50004 ◽  
Author(s):  
B. Podobnik ◽  
D. Horvatić ◽  
M. Dickison ◽  
H. E. Stanley
Keyword(s):  
Preferential Attachment ◽  
Interdependent Networks

The influence of the depth of k-core layers on the robustness of interdependent networks against cascading failures

2017 ◽  
Vol 28 (02) ◽  
pp. 1750020 ◽  
Author(s):  
Zhengcheng Dong ◽  
Yanjun Fang ◽  
Meng Tian ◽  
Zhengmin Kong
Keyword(s):  
Preferential Attachment ◽  
Core Layer ◽  
Cascading Failures ◽  
Scale Free ◽  
Interdependent Networks ◽  
The Core ◽  
Initial Load ◽  
The Hierarchical Structure ◽  
Free Network ◽  
Structure Formula

The hierarchical structure, [Formula: see text]-core, is common in various complex networks, and the actual network always has successive layers from 1-core layer (the peripheral layer) to [Formula: see text]-core layer (the core layer). The nodes within the core layer have been proved to be the most influential spreaders, but there is few work about how the depth of [Formula: see text]-core layers (the value of [Formula: see text]) can affect the robustness against cascading failures, rather than the interdependent networks. First, following the preferential attachment, a novel method is proposed to generate the scale-free network with successive [Formula: see text]-core layers (KCBA network), and the KCBA network is validated more realistic than the traditional BA network. Then, with KCBA interdependent networks, the effect of the depth of [Formula: see text]-core layers is investigated. Considering the load-based model, the loss of capacity on nodes is adopted to quantify the robustness instead of the number of functional nodes in the end. We conduct two attacking strategies, i.e. the RO-attack (Randomly remove only one node) and the RF-attack (Randomly remove a fraction of nodes). Results show that the robustness of KCBA networks not only depends on the depth of [Formula: see text]-core layers, but also is slightly influenced by the initial load. With RO-attack, the networks with less [Formula: see text]-core layers are more robust when the initial load is small. With RF-attack, the robustness improves with small [Formula: see text], but the improvement is getting weaker with the increment of the initial load. In a word, the lower the depth is, the more robust the networks will be.

Models of network formation

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Distribution ◽  
Network Formation ◽  
Preferential Attachment ◽  
Network Size ◽  
Large Network ◽  
Citation Networks ◽  
Airline Networks ◽  
Attachment Model ◽  
Delivery Networks

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

scholarly journals Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience

2021 ◽  
Vol 13 (3) ◽  
pp. 1512
Author(s):  
Yicheol Han ◽  
Stephan J. Goetz ◽  
Claudia Schmidt
Keyword(s):  
Supply Chains ◽  
Preferential Attachment ◽  
Material Balance ◽  
Service Industries ◽  
Firm Location ◽  
Commodity Flows ◽  
Geographic Space ◽  
Different Types ◽  
Gravity Equations ◽  
General Method

This article presents a spatial supply network model for estimating and visualizing spatial commodity flows that used data on firm location and employment, an input–output table of inter-industry transactions, and material balance-type equations. Building on earlier work, we proposed a general method for visualizing detailed supply chains across geographic space, applying the preferential attachment rule to gravity equations in the network context; we then provided illustrations for U.S. extractive, manufacturing, and service industries, also highlighting differences in rural–urban interdependencies across these sectors. The resulting visualizations may be helpful for better understanding supply chain geographies, as well as business interconnections and interdependencies, and to anticipate and potentially address vulnerabilities to different types of shocks.

The interface of unidirectional rewards: Enhanced cooperation within interdependent networks

2021 ◽  
Vol 402 ◽  
pp. 126151
Author(s):  
Yifan Liu ◽  
Yini Geng ◽  
Chunpeng Du ◽  
Kaipeng Hu ◽  
Chen Shen ◽  
...  
Keyword(s):  
Interdependent Networks

scholarly journals Scale free is not rare in international trade networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linqing Liu ◽  
Mengyun Shen ◽  
Chang Tan
Keyword(s):  
International Trade ◽  
Preferential Attachment ◽  
Developed Countries ◽  
Total Trade ◽  
Trade Networks ◽  
Scale Free ◽  
Weighted Networks ◽  
Free Exploration ◽  
Technology Products ◽  
Attachment Mechanism

AbstractFailing to consider the strong correlations between weights and topological properties in capacity-weighted networks renders test results on the scale-free property unreliable. According to the preferential attachment mechanism, existing high-degree nodes normally attract new nodes. However, in capacity-weighted networks, the weights of existing edges increase as the network grows. We propose an optimized simplification method and apply it to international trade networks. Our study covers more than 1200 product categories annually from 1995 to 2018. We find that, on average, 38%, 38% and 69% of product networks in export, import and total trade are scale-free. Furthermore, the scale-free characteristics differ depending on the technology. Counter to expectations, the exports of high-technology products are distributed worldwide rather than concentrated in a few developed countries. Our research extends the scale-free exploration of capacity-weighted networks and demonstrates that choosing appropriate filtering methods can clarify the properties of complex networks.

scholarly journals Likelihood-based approach to discriminate mixtures of network models that vary in time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naomi A. Arnold ◽  
Raul J. Mondragón ◽  
Richard G. Clegg
Keyword(s):  
Network Model ◽  
Change Point ◽  
Network Science ◽  
Preferential Attachment ◽  
Network Models ◽  
Explanatory Models ◽  
Growth Mechanisms ◽  
Artificial Data ◽  
Network Growth ◽  
Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

scholarly journals Greedy control of cascading failures in interdependent networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Malgorzata Turalska ◽  
Ananthram Swami
Keyword(s):  
Optimal Control ◽  
Complex Systems ◽  
Control Strategy ◽  
Cascading Failures ◽  
Optimal Control Strategy ◽  
Supercritical Regime ◽  
Interdependent Networks ◽  
Malicious Activity ◽  
Cross System ◽  
Made In

AbstractComplex systems are challenging to control because the system responds to the controller in a nonlinear fashion, often incorporating feedback mechanisms. Interdependence of systems poses additional difficulties, as cross-system connections enable malicious activity to spread between layers, increasing systemic risk. In this paper we explore the conditions for an optimal control of cascading failures in a system of interdependent networks. Specifically, we study the Bak–Tang–Wiesenfeld sandpile model incorporating a control mechanism, which affects the frequency of cascades occurring in individual layers. This modification allows us to explore sandpile-like dynamics near the critical state, with supercritical region corresponding to infrequent large cascades and subcritical zone being characterized by frequent small avalanches. Topological coupling between networks introduces dependence of control settings adopted in respective layers, causing the control strategy of a given layer to be influenced by choices made in other connected networks. We find that the optimal control strategy for a layer operating in a supercritical regime is to be coupled to a layer operating in a subcritical zone, since such condition corresponds to reduced probability of inflicted avalanches. However this condition describes a parasitic relation, in which only one layer benefits. Second optimal configuration is a mutualistic one, where both layers adopt the same control strategy. Our results provide valuable insights into dynamics of cascading failures and and its control in interdependent complex systems.

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