Universal resilience patterns in cascading load model: More capacity is not always better

2017 ◽  
Vol 28 (03) ◽  
pp. 1750041
Author(s):  
Jianwei Wang ◽  
Xue Wang ◽  
Lin Cai ◽  
Chengzhang Ni ◽  
Wei Xie ◽  
...  
Keyword(s):  
Complex Networks ◽  
Shortest Paths ◽  
Simple Graph ◽  
New Method ◽  
Cascading Failures ◽  
Load Model ◽  
Additional Load ◽  
Reasonable Explanation

We study the problem of universal resilience patterns in complex networks against cascading failures. We revise the classical betweenness method and overcome its limitation of quantifying the load in cascading model. Considering that the generated load by all nodes should be equal to the transported one by all edges in the whole network, we propose a new method to quantify the load on an edge and construct a simple cascading model. By attacking the edge with the highest load, we show that, if the flow between two nodes is transported along the shortest paths between them, then the resilience of some networks against cascading failures inversely decreases with the enhancement of the capacity of every edge, i.e. the more capacity is not always better. We also observe the abnormal fluctuation of the additional load that exceeds the capacity of each edge. By a simple graph, we analyze the propagation of cascading failures step by step, and give a reasonable explanation of the abnormal fluctuation of cascading dynamics.

Attack robustness of cascading load model in interdependent networks

2015 ◽  
Vol 26 (03) ◽  
pp. 1550030 ◽  
Author(s):  
Jianwei Wang ◽  
Yuedan Wu ◽  
Yun Li
Keyword(s):  
The Other ◽  
Network Robustness ◽  
Cascading Failures ◽  
Load Model ◽  
Interdependent Networks ◽  
Reasonable Explanation ◽  
Targeted Attacks ◽  
Initial Load ◽  
Lower Load ◽  
Local Perspective

Considering the weight of a node and the coupled strength of two interdependent nodes in the different networks, we propose a method to assign the initial load of a node and construct a new cascading load model in the interdependent networks. Assuming that a node in one network will fail if its degree is 0 or its dependent node in the other network is removed from the network or the load on it exceeds its capacity, we study the influences of the assortative link (AL) and the disassortative link (DL) patterns between two networks on the robustness of the interdependent networks against cascading failures. For better evaluating the network robustness, from the local perspective of a node we present a new measure to qualify the network resiliency after targeted attacks. We show that the AL patterns between two networks can improve the robust level of the entire interdependent networks. Moreover, we obtain how to efficiently allocate the initial load and select some nodes to be protected so as to maximize the network robustness against cascading failures. In addition, we find that some nodes with the lower load are more likely to trigger the cascading propagation when the distribution of the load is more even, and also give the reasonable explanation. Our findings can help to design the robust interdependent networks and give the reasonable suggestion to optimize the allocation of the protection resources.

THE METHOD OF ESTIMATION FRACTAL DIMENSION OF THE TELECOMMUNICATION NETWORK

2021 ◽  
Author(s):  
Ф.Х. НАХЛИ ◽  
А.И. ПАРАМОНОВ
Keyword(s):  
Fractal Dimension ◽  
Comparative Analysis ◽  
Telecommunication Network ◽  
Shortest Paths ◽  
Network Connectivity ◽  
Telecommunication Networks ◽  
New Method ◽  
Topological Properties

Анализируется фрактальная размерность (ФР) сети связи и ее использование для исследования и планирования сетей связи. Рассматривается применение метода «выращивания кластера» для оценки ФР и предлагается новый метод определения ФР сети, основанный на оценивании связности сети путем поиска кратчайших путей. Показано, что оценка ФР сети является дополнительной характеристикой, отражающей топологические свойства сети. Дается сравнительный анализ предложенного метода и «выращивания кластера». Полученные результаты позволяют выбрать метод и получить оценки ФР сети в зависимости от ее особенностей. The paper analyzes the fractal dimension of the network and its use for telecommunication networks research and planning. The analysis of the "cluster growing" method for assessing the fractal dimension is given and a new method for assessing the fractal dimensionof anetwork is proposed, based onassessing the network connectivity by finding the shortest paths. The article shows that the assessment of the fractal dimension of the network is an additional characteristic that reflects the topological properties of the network. Comparative analysis of the proposed method and "cluster growing" is given. The results obtained make it possible to select a method and obtain estimates of the fractal dimension of the network, depending on its features.

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

Node Recovery from Cascading Failures in Complex Networks Based on Q-model

2021 ◽  
pp. 717-723
Author(s):  
Hao Shen ◽  
Shiwen Sun ◽  
Jin Zhang ◽  
Chengyi Xia
Keyword(s):  
Complex Networks ◽  
Cascading Failures ◽  
Q Model

COMMUNITY DETECTION IN NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 361-367 ◽  
Author(s):  
C. O. DORSO ◽  
A. D. MEDUS
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
New Method

The problem of community detection is relevant in many disciplines of science. A community is usually defined, in a qualitative way, as a subset of nodes of a network which are more connected among themselves than to the rest of the network. In this article, we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al. [2004] and we show that this local definition properly describes the communities observed experimentally in two typical social networks.

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