Centrality in earthquake multiplex networks

Lotfi, Nastaran; Darooneh, Amir H.; Rodrigues, Francisco A.

doi:10.1063/1.5001469

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…A prominent example is the generalization of graphs to multilayer networks [8,18,55,84], and there have been many efforts to extend centrality measures to multiplex and temporal networks [2,27,38,39,42,54,63,72,82,92,94,96,97,101,102,107,118,119]. Multilayer network centralities have been used in the study of diverse applications, including social networks [14,17,42,66,67], transportation systems [22,48,103,113], economic systems [5,23,24], neural systems [6,20,48,120], and signal processing of geological time series [65]. Moreover, many techniques in centrality analysis are closely connected to the study of various dynamical processes (including in multilayer networks), such as random walks [25,33,35,42,71,78], information spreading…”

mentioning

confidence: 99%

Tunable Eigenvector-Based Centralities for Multiplex and Temporal Networks

Taylor¹,

Porter²,

Mucha³

2019

Preprint

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Characterizing the importances (i.e., centralities) of nodes in social, biological, and technological networks is a core topic in both network science and data science. We present a linear-algebraic framework that generalizes eigenvector-based centralities, including PageRank and hub/authority scores, to provide a common framework for two popular classes of multilayer networks: multiplex networks (which have layers that encode different types of relationships) and temporal networks (in which the relationships change over time). Our approach involves the study of joint, marginal, and conditional "supracentralities" that one can calculate from the dominant eigenvector of a supracentrality matrix , which couples centrality matrices that are associated with individual network layers. We extend this prior work (which was restricted to temporal networks with layers that are coupled by adjacent-in-time coupling) by allowing the layers to be coupled through a (possibly asymmetric) interlayer-adjacency matrix Ã, where the entry Ãtt ≥ 0 encodes the coupling between layers t and t . Our framework provides a unifying foundation for centrality analysis of multiplex and temporal networks; it also illustrates a complicated dependency of the supracentralities on the topology and weights of interlayer coupling. By scaling Ã by an interlayercoupling strength ω ≥ 0 and developing a singular perturbation theory for the limits of weak (ω → 0 + ) and strong coupling (ω → ∞), we also reveal an interesting dependence of supracentralities on the dominant left and right eigenvectors of Ã. We provide additional theoretical and practical insights by applying our framework to two empirical data sets: a multiplex network of airline transportation in Europe and a temporal network that encodes the graduation and hiring of mathematical scientists at United States universities.

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mentioning

confidence: 99%

Tunable Eigenvector-Based Centralities for Multiplex and Temporal Networks

Taylor¹,

Porter²,

Mucha³

2019

Preprint

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“…ese two metrics do not exist independently. We decided to use the sum of earthquake magnitudes [29] as an essential measure to compare the similarities and differences of the network construction methods. We will compare the network characteristics number of nodes, average degree, betweenness, coreness, and entropy of the three networks.…”

Section: Evolutionmentioning

confidence: 99%

Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

Shah

Wei

et al. 2021

Complexity

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The approach of the complex network has well described seismic complex systems. In this paper, this is the first time three classical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. They all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures show a similar correlation with seismicity dynamics, but one measure shows a different result. These results show that different network construction methods will present some similarities and differences in network properties. This situation needs to be considered, especially when discussing a predictive indicator of seismicity.

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“…Nowadays, the complex networks theory appears as a useful method for studying many phenomena in various disciplines of science and technology [35][36][37][38]. There are many systems in nature which can be modeled by complex networks such as neural networks [39], cells [40], internet [41,42], earthquakes [43][44][45][46][47][48] and climate network [49][50][51]. Every network is characterized by knowing the nodes and how they are connected to one other.…”

Section: Complex Networkmentioning

confidence: 99%

The network structure affects the fixation probability when it couples to the birth-death dynamics in finite population

2021

Self Cite

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The study of evolutionary dynamics on graphs is an interesting topic for researchers in various fields of science and mathematics. In systems with finite population, different model dynamics are distinguished by their effects on two important quantities: fixation probability and fixation time. The isothermal theorem declares that the fixation probability is the same for a wide range of graphs and it only depends on the population size. This has also been proved for more complex graphs that are called complex networks. In this work, we propose a model that couples the population dynamics to the network structure and show that in this case, the isothermal theorem is being violated. In our model the death rate of a mutant depends on its number of neighbors, and neutral drift holds only in the average. We investigate the fixation probability behavior in terms of the complexity parameter, such as the scale-free exponent for the scale-free network and the rewiring probability for the small-world network.

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Centrality in earthquake multiplex networks

Cited by 14 publications

References 25 publications

Tunable Eigenvector-Based Centralities for Multiplex and Temporal Networks

Tunable Eigenvector-Based Centralities for Multiplex and Temporal Networks

Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

The network structure affects the fixation probability when it couples to the birth-death dynamics in finite population

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