Robustness analysis of bimodal networks in the whole range of degree correlation

Mizutaka, Shogo; Tanizawa, Toshihiro

doi:10.1103/physreve.94.022308

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Several scholars have proposed various effective methods for financial network construction. For example, Gan et al applied the threshold method to reduce noise in a pearson correlation network [55]. Further, Marti et al proposed using the MST method and PCC to construct the network, which resolved the shortcomings of artificial and subjective threshold selection, and likewise simplified the topology of the financial network (M-1) [56].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Financial crisis prediction based on multilayer supervised network analysis

Lü

Wang

2022

Front. Phys.

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Financial crisis prediction is essential in preventing financial problems as its monitoring indicators help regulators judge the probability of future crises. In this context, the activities of the scientific community have been focused on the dynamics of single/multiple sequences and utilized unsupervised/supervised methods for financial crisis prediction. It is noteworthy that the cross-correlation between the risks of multiple economic entities makes financial network analysis paramount in crisis prediction. Focusing on this point, we propose a multilayer supervised network analysis (MSNA) method to train the multilayer network, and select the most suitable layer for financial crisis prediction. Specifically, we use 37 crucial stock market indices from 4 continents to create successive multilayer financial networks with 120-day windows and 1-day step by Pearson cross-correlation (PCC), variance decompositions (VD), transfer entropy (TE), minimum spanning tree (MST), directed MST (DMST), planar maximally filtered graph (PMFG) and directed PMFG (DPMFG) methods. Based on the multilayer network, we embed the graph neural network classification (GNNC) model and train the dynamic multilayer networks at each window scale (240,120, and 60 days). Finally, we conclude that the accuracy of the short window (60 days) is significantly higher than that of the long window. The network constructed by PCC with MST is the most suitable for short sequence (60 days) crisis prediction (AUC = 0.959), and the network constructed by TE with DMST is the most suitable for long sequence (240 days) crisis prediction (AUC = 0.772).

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Section: Conclusion and Discussionmentioning

confidence: 99%

Financial crisis prediction based on multilayer supervised network analysis

Lü

Wang

2022

Front. Phys.

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“…It should be noted that percolation theory methods are widely used in various fields of science, not only in mathematics [21,22], physics [23,24] and computer science [25,26], descriptions of the spread of virus epidemics in networks [27,28], but also for example, in earth sciences [29][30][31], analysis of social network structures [32][33][34], and many others.…”

Section: Theoretical Methods Within Percolation Theorymentioning

confidence: 99%

The Influence of a Network’s Spatial Symmetry, Topological Dimension, and Density on Its Percolation Threshold

2019

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Analyses of the processes of information transfer within network structures shows that the conductivity and percolation threshold of the network depend not only on its density (average number of links per node), but also on its spatial symmetry groups and topological dimension. The results presented in this paper regarding conductivity simulation in network structures show that, for regular and random 2D and 3D networks, an increase in the number of links (density) per node reduces their percolation threshold value. At the same network density, the percolation threshold value is less for 3D than for 2D networks, whatever their structure and symmetry may be. Regardless of the type of networks and their symmetry, transition from 2D to 3D structures engenders a change of percolation threshold by a value exp{−(d − 1)} that is invariant for transition between structures, for any kind of network (d being topological dimension). It is observed that in 2D or 3D networks, which can be mutually transformed by deformation without breaking and forming new links, symmetry of similarity is observed, and the networks have the same percolation threshold. The presence of symmetry axes and corresponding number of symmetry planes in which they lie affects the percolation threshold value. For transition between orders of symmetry axes, in the presence of the corresponding planes of symmetry, an invariant exists which contributes to the percolation threshold value. Inversion centers also influence the value of the percolation threshold. Moreover, the greater the number of pairs of elements of the structure which have inversion, the more they contribute to the fraction of the percolation threshold in the presence of such a center of symmetry. However, if the center of symmetry lies in the plane of mirror symmetry separating the layers of the 3D structure, the mutual presence of this group of symmetry elements do not affect the percolation threshold value. The scientific novelty of the obtained results is that for different network structures, it was shown that the percolation threshold for the blocking of nodes problem could be represented as an additive set of invariant values, that is, as an algebraic sum, the value of the members of which is stored in the transition from one structure to another. The invariant values are network density, topological dimension, and some of the elements of symmetry (axes of symmetry and the corresponding number of symmetry planes in which they lie, centers of inversion).

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“…In the next section, we discuss the transition between these two states in the present model. We run the model on correlated bimodal networks to investigate the effect of degree correlation on the synergistic epidemics [41,42]. A bimodal network consists of two types of nodes with different degrees; type-1 nodes with degree k 1 and type-2 nodes with degree k 2 (≤ k 1 ).…”

Section: Modelmentioning

confidence: 99%

“…. The Pearson's coefficient r kk ′ measures the degree-degree correlations, but we note that it gives the assortativity coefficient r of a correlated bimodal network [42].…”

Section: Modelmentioning

confidence: 99%

Synergistic epidemic spreading in correlated networks

Mizutaka¹,

Mori²,

Hasegawa³

2021

Preprint

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We investigate the effect of degree correlation on a susceptible-infected-susceptible (SIS) model with a nonlinear cooperative effect (synergy) in infectious transmissions. In a mean-field treatment of the synergistic SIS model on a bimodal network with tunable degree correlation, we identify a discontinuous transition that is independent of the degree correlation strength unless the synergy is absent or extremely weak. Regardless of synergy (absent or present), a positive and negative degree correlation in the model reduces and raises the epidemic threshold, respectively. For networks with a strongly positive degree correlation, the mean-field treatment predicts the emergence of two discontinuous jumps in the steady-state infected density. To test the mean-field treatment, we provide approximate master equations of the present model, which accurately describe the synergistic SIS dynamics. We quantitatively confirm all qualitative predictions of the mean-field treatment in numerical evaluations of the approximate master equations.

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Robustness analysis of bimodal networks in the whole range of degree correlation

Cited by 8 publications

References 23 publications

Financial crisis prediction based on multilayer supervised network analysis

Financial crisis prediction based on multilayer supervised network analysis

The Influence of a Network’s Spatial Symmetry, Topological Dimension, and Density on Its Percolation Threshold

Synergistic epidemic spreading in correlated networks

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