scholarly journals How breadth of degree distribution influences network robustness: Comparing localized and random attacks

2015 ◽  
Vol 92 (3) ◽  
Author(s):  
Xin Yuan ◽  
Shuai Shao ◽  
H. Eugene Stanley ◽  
Shlomo Havlin
Keyword(s):  
Degree Distribution ◽  
Network Robustness

scholarly journals OPTIMIZATION OF SCALE-FREE NETWORK FOR RANDOM FAILURES

2006 ◽  
Vol 20 (14) ◽  
pp. 815-820 ◽  
Author(s):  
JIAN-GUO LIU ◽  
ZHONG-TUO WANG ◽  
YAN-ZHONG DANG
Keyword(s):  
Network Design ◽  
Degree Distribution ◽  
Design Guidelines ◽  
Network Robustness ◽  
Optimization Strategy ◽  
Scale Free Network ◽  
Scale Free ◽  
Optimal Value ◽  
Free Network ◽  
Random Failures

It has been found that the networks with scale-free degree distribution are very resilient for random failures. The purpose of this work is to determine the network design guidelines which maximize the network robustness for random failures when the average number of links per node of the network is constant. The optimal value of the degree distribution exponent and the minimum connectivity to different network sizes are given in this paper. Finally, the optimization strategy on how to improve the evolving network robustness is given.

Improving the robustness of scale-free networks by maintaining community structure

2019 ◽  
Vol 7 (6) ◽  
pp. 838-864 ◽  
Author(s):  
Marzieh Mozafari ◽  
Mohammad Khansari
Keyword(s):  
Community Structure ◽  
Degree Distribution ◽  
Network Performance ◽  
Network Robustness ◽  
Degree Centrality ◽  
Functional Modules ◽  
Scale Free ◽  
Scale Free Networks ◽  
Artificial Network ◽  
Attack Strategy

Abstract Scale-free networks, which play an important role in modelling human activities, are always suffering from intentional attacks which have serious consequences on their functionality. Degree distribution and community structure are two distinguishing characteristics of these networks considered in optimizing network robustness process recently. Since community structure is known as functional modules in some networks, modifying them during the improving network robustness process may affect network performance. We propose a preferential rewiring method to improve network robustness which not only keeps degree distribution unchanged but also preserves community structure and decreases the number of rewired edges at the same time. At first, the robustness of each community is improved by applying a smart rewiring method based on the neighbourhood of nodes. Then, relations between communities are gotten more robust with a preferential rewiring based on degree and betweenness hybrid centrality of nodes. This method was applied to several types of networks including Dolphins, WU-PowerGrid and US-Airline as real-world networks and Lancichinetti–Fortunato–Radicchi benchmark model as an artificial network with the scale-free property. The results show that the proposed method enhances the robustness of all networks against degree centrality attacks between 50% and 80% and betweenness centrality attacks between 30% and 70%. Whereas, in all cases, community structure preserved more than 50%. In comparison with previous studies, the proposed method can improve network robustness more significantly and decrease the number of rewires. It also is not dependent on the attack strategy.

scholarly journals Influence of Interlink Topology on Multilayer Network Robustness

2020 ◽  
Vol 12 (3) ◽  
pp. 1202
Author(s):  
Fang Zhou ◽  
Xiang He ◽  
Yongbo Yuan ◽  
Mingyuan Zhang
Keyword(s):  
Degree Distribution ◽  
Network Robustness ◽  
Multilayer Networks ◽  
Actual System ◽  
Multilayer Network ◽  
Node Number ◽  
Coupled Networks ◽  
Calculation Point ◽  
Global Robustness ◽  
Network Experiment

Cascading failures between interdependent multilayer networks are being widely studied, especially the trend of robustness caused by the interlinks between networks. However, few researchers pay attention to the effect of the interlink topology on the robustness of coupled networks, which is a critical interlink factor of multilayer networks. In this study, the method frame of multilayer network experiment simulation is given. Through numerical simulation and actual network simulation, the exhaustive method is used to enumerate all the patterns of interlink topological relations of multilayer networks (three-layer or more). The research verifies that the interlink topology affects the global robustness and that there exists a fragile interlink pattern in the patterns of interlink topologies. The star-like interlink pattern with the most uneven interlink-degree distribution leads to the weakest robustness; the pattern with average interlink-degree distribution reveals good global stability as a loop-like pattern or entire interlink pattern. In addition, the influence of interlink topology is independent. The simulation results are not affected by the network layer number and intraparameters (including the network-generated form, each layer of network node number, and average degree of each layer of network). Thus, ignoring the interlink topology may result in the actual system suddenly becoming vulnerable before the theoretical calculation point. Interlink topology as an independent factor affecting the robustness of multilayer networks should be paid more attention.

Analysis of Oncogene Protein Structure Using Small World Network Concept

2020 ◽  
Vol 15 (7) ◽  
pp. 732-740
Author(s):  
Neetu Kumari ◽  
Anshul Verma
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Degree Distribution ◽  
Protein Structures ◽  
Small World ◽  
Extreme Condition ◽  
Centrality Measures ◽  
Small World Network ◽  
Network Concept ◽  
Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

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 Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

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.

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