scholarly journals Mitigation of cascading failures in complex networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alex Smolyak ◽  
Orr Levy ◽  
Irena Vodenska ◽  
Sergey Buldyrev ◽  
Shlomo Havlin
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
Failure Mechanisms ◽  
Local Network ◽  
Financial Networks ◽  
Cascading Failures ◽  
Real Network ◽  
Critical Nodes ◽  
System Collapse ◽  
Mitigation Techniques

Abstract Cascading failures in many systems such as infrastructures or financial networks can lead to catastrophic system collapse. We develop here an intuitive, powerful and simple-to-implement approach for mitigation of cascading failures on complex networks based on local network structure. We offer an algorithm to select critical nodes, the protection of which ensures better survival of the network. We demonstrate the strength of our approach compared to various standard mitigation techniques. We show the efficacy of our method on various network structures and failure mechanisms, and finally demonstrate its merit on an example of a real network of financial holdings.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

Improved influential nodes identification in complex networks

2021 ◽  
pp. 1-9
Author(s):  
Shi Dong ◽  
Wengang Zhou
Keyword(s):  
Theoretical Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Information Entropy ◽  
Network System ◽  
Weighted Degree ◽  
Identification Methods ◽  
Hybrid Mechanism ◽  
Influential Nodes ◽  
Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

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.

scholarly journals Risk Aversion and Social Networks

2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Jaromír Kovářík ◽  
Marco J. van der Leij
Keyword(s):  
Risk Aversion ◽  
Undergraduate Students ◽  
Network Structure ◽  
Network Formation ◽  
Empirical Finding ◽  
Local Network ◽  
Network Clustering ◽  
Strongly Correlated ◽  
Social Network Structure ◽  
The One

AbstractThis paper first investigates empirically the relationship between risk aversion and social network structure in a large group of undergraduate students. We find that risk aversion is strongly correlated to local network clustering, that is, the probability that one has a social tie to friends of friends. We then propose a network formation model that generates this empirical finding, suggesting that locally superior information on benefits makes it more attractive for risk averse individuals to link to friends of friends. Finally, we discuss implications of this model. The model generates a positive correlation between local network clustering and benefits, even if benefits are distributed independently ex ante. This provides an alternative explanation of this relationship to the one given by the social capital literature. We also establish a linkage between the uncertainty of the environment and the network structure: risky environments generate more clustered and more unequal networks in terms of connectivity.

An Introduction to Complex Networks

2013 ◽  
Author(s):  
Jordi Bascompte ◽  
Pedro Jordano
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Network Structure ◽  
Ecological Networks ◽  
Broad Context ◽  
Network Approach ◽  
Mutualistic Networks ◽  
Plant Animal Interactions

Mutualisms can involve dozens, even hundreds, of species and this complexity has precluded a serious community-wide approach to plant–animal interactions. The most straightforward way to describe such an interacting community is with a network of interactions. In this approach, species are represented as nodes of two types: plants and animals. This chapter provides the tools and concepts for characterizing mutualistic networks and placing them into a broad context. This serves as a background with which to understand the structure of mutualistic networks. The discussions cover a network approach to complex systems, measures of network structure, models of network buildup, and ecological networks.

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

scholarly journals GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i464-i473
Author(s):  
Kapil Devkota ◽  
James M Murphy ◽  
Lenore J Cowen
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
Global Network ◽  
Local Network ◽  
Supplementary Information ◽  
Network Data ◽  
Ppi Network ◽  
Diffusion State

Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Degree difference: a simple measure to characterize structural heterogeneity in complex networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Amirhossein Farzam ◽  
Areejit Samal ◽  
Jürgen Jost
Keyword(s):  
Complex Networks ◽  
Structural Properties ◽  
Degree Sequence ◽  
Structural Heterogeneity ◽  
Local Network ◽  
Basic Unit ◽  
Local Geometry ◽  
Scale Free ◽  
Mixing Patterns ◽  
Topological Robustness

AbstractDespite the growing interest in characterizing the local geometry leading to the global topology of networks, our understanding of the local structure of complex networks, especially real-world networks, is still incomplete. Here, we analyze a simple, elegant yet underexplored measure, ‘degree difference’ (DD) between vertices of an edge, to understand the local network geometry. We describe the connection between DD and global assortativity of the network from both formal and conceptual perspective, and show that DD can reveal structural properties that are not obtained from other such measures in network science. Typically, edges with different DD play different structural roles and the DD distribution is an important network signature. Notably, DD is the basic unit of assortativity. We provide an explanation as to why DD can characterize structural heterogeneity in mixing patterns unlike global assortativity and local node assortativity. By analyzing synthetic and real networks, we show that DD distribution can be used to distinguish between different types of networks including those networks that cannot be easily distinguished using degree sequence and global assortativity. Moreover, we show DD to be an indicator for topological robustness of scale-free networks. Overall, DD is a local measure that is simple to define, easy to evaluate, and that reveals structural properties of networks not readily seen from other measures.

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