arXiv subjects

Shlomo Havlin

Publications and source records attributed to Shlomo Havlin.

At least 55 records · Page 3Linked to original sources

arXiv2022

Teleconnections among Tipping Elements in the Earth System

Tipping elements of the Earth system may shift abruptly and irreversibly from one state to another at tipping points, resulting in a growing threat to our society. Yet, it is not fully clear how to assess and quantify the influence of a tipping element and how to explore the teleconnections between different tipping elements. To fill this knowledge gap, we propose a climate network approach to quantitatively analyze the global impacts of a prominent tipping element, the Amazon Rainforest Area (ARA). We find that regions, such as, the Tibetan Plateau (TP) and West Antarctic ice sheet, are characterized by higher network weighted links and exhibit strong correlations with the ARA. We then identify a teleconnection propagation path between the ARA and the TP. This path is robust under climate change as simulated by various climate models of CMIP5 and CMIP6. In addition, we detect early warning signals for critical transition in the snow cover extent on the Tibetan Plateau by applying critical slowing down indicators, lag-1 autocorrelation and detrended fluctuation analysis. We find that the snow cover of the TP has been losing stability since 2008, revealing that the TP is operating like a tipping element and approaching a potential tipping point. We further uncover that various climate extremes between the ARA and the TP are significantly synchronized under climate change. Our framework provides new insights into how tipping elements are linked to each other and into the potential predictability of cascading tipping dynamics.

physics.soc-ph↗

arXiv2022

Topological-temporal properties of evolving networks

Many real-world complex systems including human interactions can be represented by temporal (or evolving) networks, where links activate or deactivate over time. Characterizing temporal networks is crucial to compare such systems and to study the dynamical processes unfolding on them. A systematic method to characterize simultaneously the temporal and topological relations of active links (also called contacts or events), in order to compare different real-world networks and to detect their common patterns or differences is still missing. In this paper, we propose a method to characterize to what extent contacts that happen close in time occur also close in topology. Specifically, we study the interrelation between temporal and topological properties of contacts from three perspectives: (1) the autocorrelation of the time series recording the total number of contacts happened at each time step in a network; (2) the interplay between the topological distance and interevent time of two contacts; (3) the temporal correlation of contacts within local neighborhoods beyond a node pair. By applying our method on 13 real-world temporal networks, we found that temporal-topological correlation of contacts is more evident in virtual contact networks than in physical contact ones. This could be due to the lower cost and easier access of online communications than physical interactions, allowing and possibly facilitating social contagion, i.e., interactions of one individual may influence the activity of its neighbors. We also identify different patterns between virtual and physical networks and among physical contact networks at, e.g., school and workplace, in the formation of correlation in local neighborhoods. Detected patterns and differences may further inspire the development of more realistic temporal network models, that could reproduce jointly temporal and topological properties of contacts.

physics.soc-ph↗

arXiv2022

Impactful scientists have higher tendency to involve collaborators in new topics

In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, resources, and for performing interdisciplinary research. Although collaboration networks have been intensively studied, the question of how individual scientists choose collaborators to study a new research topic remains almost unexplored. Here, we investigate the statistics and mechanisms of collaborations of individual scientists along their careers, revealing that, in general, collaborators are involved in significantly fewer topics than expected from controlled surrogate. In particular, we find that highly productive scientists tend to have higher fraction of single-topic collaborators, while highly cited, i.e., impactful, scientists have higher fraction of multi-topic collaborators. We also suggest a plausible mechanism for this distinction. Moreover, we investigate the cases where scientists involve existing collaborators into a new topic. We find that compared to productive scientists, impactful scientists show strong preference of collaboration with high impact scientists on a new topic. Finally, we validate our findings by investigating active scientists in different years and across different disciplines.

cs.DL↗

arXiv2022

External field and critical exponents in controlling dynamics on complex networks

Dynamical processes on complex networks, ranging from biological, technological and social systems, show phase transitions between distinct global states of the system. Often, such transitions rely upon the interplay between the structure and dynamics that takes place on it, such that weak connectivity, either sparse network or frail interactions, might lead to global activity collapse, while strong connectivity leads to high activity. Here, we show that controlling dynamics of a fraction of the nodes in such systems acts as an external field in a continuous phase transition. As such, it defines corresponding critical exponents, both at equilibrium and of the transient time. We find the critical exponents for a general class of dynamics using the leading orders of the dynamic functions. By applying this framework to three examples, we reveal distinct universality classes.

physics.soc-ph↗

arXiv2022

Fractal fluctuations at mixed-order transitions in interdependent networks

We study the geometrical features of the order parameter's fluctuations near the critical point of mixed-order phase transitions in randomly interdependent spatial networks. In contrast to continuous transitions, where the structure of the order parameter at criticality is fractal, in mixed-order transitions the structure of the order parameter is known to be compact. Remarkably, we find that although being compact, the fluctuations of the order parameter close to mixed-order transitions are fractal up to a well-defined correlation length $ξ'$, which diverges when approaching the critical threshold. We characterize the self-similar nature of these critical fluctuations through their fractal dimension, $d_f'=3d/4$, and correlation length exponent, $ν'=2/d$, where $d$ is the dimension of the system. By means of percolation and magnetization, we demonstrate that $d_f'$ and $ν'$ are independent on the symmetry of the underlying process for any $d$ of the underlying networks.

cond-mat.dis-nn↗

arXiv2022

Reviving a failed network through microscopic interventions

From mass extinction to cell death, complex networked systems often exhibit abrupt dynamic transitions between desirable and undesirable states. Such transitions are often caused by topological perturbations, such as node or link removal, or decreasing link strengths. The problem is that reversing the topological damage, namely retrieving the lost nodes or links, or reinforcing the weakened interactions, does not guarantee the spontaneous recovery to the desired functional state. Indeed, many of the relevant systems exhibit a hysteresis phenomenon, remaining in the dysfunctional state, despite reconstructing their damaged topology. To address this challenge, we develop a two-step recovery scheme: first - topological reconstruction to the point where the system can be revived, then dynamic interventions, to reignite the system's lost functionality. Applying this method to a range of nonlinear network dynamics, we identify the recoverable phase of a complex system, a state in which the system can be reignited by microscopic interventions, for instance, controlling just a single node. Mapping the boundaries of this dynamical phase, we obtain guidelines for our two-step recovery.

nlin.AO↗

arXiv2022

Improving robustness of spatial networks via reinforced nodes

Many real-world networks are embedded in space, and their resilience in the presence of reinforced nodes has not been studied. Here we model such networks using a spatial network model that have an exponential distribution of link length $r$ having a characteristic length $ζ$. We find that reinforced nodes can significantly increase the resilience of the networks which varies with strength of spatial embedding. We also study different reinforced node distribution strategies for improving the network resilience. Interestingly, we find that the best strategy is highly dependent on the stage of the percolation process, i.e., the expected fraction of failures. Finally, we show that the reinforced nodes are analogous to an external field in percolation phase transition i.e., having the same critical exponents and that the critical exponents satisfy Widom's relation.

physics.soc-ph↗

arXiv2022

Delayed Impact of Interdisciplinary Research

Interdisciplinary research increasingly fuels innovation, and is considered to be a key to tomorrow breakthrough. Yet little is known about whether interdisciplinary research manifests delayed impact. Here, we use the time to reach the citation peak to quantify the highest impact time and citation dynamics, and examine its relationship with interdisciplinarity. Using large scale publication datasets, our results suggest that interdisciplinary papers show significant delayed impact both microscopically per paper and macroscopically collectively, as it takes longer time for interdisciplinary papers to reach their citation peak. Furthermore, we study the underlying forces of such delayed impact, finding that the effect goes beyond the Matthew effect (i.e., the rich-get-richer effect). Finally, we find that team size and content conventionality only partly account for this effect. Overall, our results suggest that governments, research administrators, funding agencies should be aware of this general feature of interdisciplinary science, which may have broad policy implications.

cs.DL↗

arXiv2022

Sustaining a network by controlling a fraction of nodes

Multi-stability is a widely observed phenomenon in real complex networked systems, such as technological infrastructures, ecological systems, gene regulation, transportation and more. When a system functions normally but there exists also a potential state with abnormal low activity, although the system is at equilibrium it might make a transition into the low activity undesired state due to external disturbances and perturbations. Thus, such a system can be regarded as unsustainable, due to the danger of falling into the potential inactive state. Here we explore, analytically and by simulations, how supporting the activity of a fraction $ρ$ of nodes can turn an unsustainable system to be sustainable by eliminating the inactive potential stable state. We thus unveil a new sustainability phase diagram in the presence of a fraction of controlled nodes $ρ$. This phase diagram could provide guidelines to sustain a network by external intervention and/or by strengthening the connectivity of the network.

physics.soc-ph↗

arXiv2022

Epidemics on evolving networks with varying degrees

Epidemics on complex networks is a widely investigated topic in the last few years, mainly due to the last pandemic events. Usually, real contact networks are dynamic, hence much effort has been invested in studying epidemics on evolving networks. Here we propose and study a model for evolving networks based on varying degrees, where at each time step a node might get, with probability $r$, a new degree and new neighbors according to a given degree distribution, instead of its former neighbors. We find analytically, using the generating functions framework, the epidemic threshold and the probability for a macroscopic spread of disease depending on the rewiring rate $r$. Our analytical results are supported by numerical simulations. We find surprisingly that the impact of the rewiring rate $r$ has qualitative different trends for networks having different degree distributions. That is, in some structures, such as random regular networks the dynamics enhances the epidemic spreading while in others such as scale free the dynamics reduces the spreading. In addition, for scale-free networks, we reveal that fast dynamics of the network, $r=1$, changes the epidemic threshold to nonzero rather than zero found for $r<1$, which is similar to the known case of $r=0$, i.e., a static network. Finally, we find the epidemic threshold also for a general distribution of the recovery time.

physics.soc-ph↗

arXiv2022

Percolation on spatial anisotropic networks

Many realistic systems such as infrastructures are characterized by spatial structure and anisotropic alignment. Here we propose and study a model for dealing with such characteristics by introducing a parameter that controls the strength of the anisotropy in the spatial network. This parameter is added to an existing isotropic model used to describe networks under spatial constraints, thus generalizing the spatial model to take into account both spatial and anisotropic features. We study the resilience of such networks by using a percolation process and find that anisotropy has a negative impact on a network's robustness. In addition, our results suggest that the anisotropy in this model does not affect the critical exponent of the correlation length, $ν$, which remains the same as the known $ν$ in 2D isotropic lattices.

physics.soc-ph↗

arXiv2021

Identification and prioritization of urban traffic bottlenecks

The increasing urbanization process we have been witnessing in the last decades is accompanied by a significant increase in traffic congestion in cities around the world. The effect of the congestion is represented in the enormous time people spent on roads leading to significant money waste and air pollution. Here, we present a new methodology for identification, cost evaluation, and thus, prioritization of congestion sources, the jam bottlenecks. It extends existing methods as it is based on network analysis of the entire road network and can be applied to different traffic models. Our results show that the macro-stability, presented by scaling characteristics of the traffic bottlenecks, overshadows the existence of meso-dynamics, where the bottlenecks change their location in time and space. This means that to plan and manage traffic jams in different locations and at different times, it is required to implement a framework, as developed here, that tracks traffic and evaluates the relative effect of each evolving bottleneck on the entire road network.

physics.soc-ph↗

arXiv2021

Scaling of variations in traveling distances and times of taxi routes

The importance of understanding human mobility patterns has led many studies to examine their spatial-temporal scaling laws. These studies mainly reveal that human travel can be highly non-homogeneous with power-law scaling distributions of distances and times. However, investigating and quantifying the extent of variability in time and space when traveling the same air distance has not been addressed so far. Using taxi data from five large cities, we focus on several novel measures of distance and time to explore the spatio-temporal variations of taxi travel routes relative to their typical routes during peak and nonpeak periods. To compare all trips using a single measure, we calculate the distributions of the ratios between actual travel distances and the average travel distance as well as between actual travel times and the average travel time for all origin destinations (OD) during peak and nonpeak periods. In this way, we measure the scaling of the distribution of all single trip paths with respect to their mean trip path. Our results surprisingly demonstrate very broad distributions for both the distance ratio and time ratio, characterized by a long-tail power-law distribution. Moreover, all analyzed cities have larger exponents in peak hours than in nonpeak hours. We suggest that the interesting results of shorter trip lengths and times, characterized by larger exponents during rush hours, are due to the higher availability of travelers in rush hours compared to non-rush hours...

physics.soc-ph↗

arXiv2021

Asymmetry in earthquake interevent time intervals

Here we focus on a basic statistical measure of earthquake catalogs that has not been studied before, the asymmetry of interevent time series (e.g., reflecting the tendency to have more aftershocks than spontaneous earthquakes). We define the asymmetry metric as the ratio between the number of positive interevent time increments minus negative increments and the total (positive plus negative) number of increments. Such asymmetry commonly exists in time series data for non-linear geophysical systems like river flow which decays slowly and increases rapidly. We find that earthquake interevent time series are significantly asymmetric, where the asymmetry function exhibits a significant crossover to weak asymmetry at large lag-index. We suggest that the Omori law can be associated with the large asymmetry at short time intervals below the crossover whereas overlapping aftershock sequences and the spontaneous events can be associated with a fast decay of asymmetry above the crossover. We show that the asymmetry is better reproduced by a recently modified ETAS model with two triggering processes in comparison to the standard ETAS model which only has one.

physics.geo-ph↗

arXiv2021

Effects of mobility restrictions during COVID19 in Italy

To reduce the spread and the effect of the COVID-19 global pandemic, non-pharmaceutical interventions (NPIs) have been adopted on multiple occasions by governments. In particular lockdown policies, i.e., generalized mobility restrictions, have been employed to fight the first wave of the pandemic. We analyze data reflecting mobility levels over time in Italy before, during and after the national lockdown, in order to assess some direct and indirect effects. By applying methodologies based on percolation and network science approaches, we find that the typical network characteristics, while very revealing, do not tell the whole story. In particular, the Italian mobility network during lockdown has been damaged much more than node- and edge-level metrics indicate. Additionally, many of the main Provinces of Italy are affected by the lockdown in a surprisingly similar fashion, despite their geographical and economic dissimilarity. Based on our findings we offer an approach to estimate unavailable high-resolution economic dimensions, such as real time Province-level GDP, based on easily measurable mobility information.

physics.soc-ph↗

arXiv2021

Optimization of robustness based on reinforced nodes in a modular network

Many systems such as critical infrastructure exhibit a modular structure with many links within the modules and few links between them. One approach to increase the robustness of these systems is to reinforce a fraction of the nodes in each module, so that the reinforced nodes provide additional needed sources for themselves as well as for their nearby neighborhood. Since reinforcing a node can be an expensive task, the efficiency of the decentralization process by reinforced nodes is vital. In our study we analyze a new model which combines both above mentioned features of real complex systems - modularity and reinforced nodes. Using tools from percolation theory, we derived an analytical solution for any partition of reinforced nodes; between nodes which have links that connect them to other modules ("inter-nodes") and nodes which have connections only within their modules ("intra-nodes"). Among our results, we find that near the critical percolation point ($p\approx p_c$) the robustness is greatly affected by the distribution. In particular, we find a partition of reinforced nodes which yields an optimal robustness and we show that the optimal partition remains constant for high average degrees.

physics.soc-ph↗

arXiv2021

Spatial correlations in geographical spreading of COVID-19 in USA

The global spread of the COVID-19 pandemic has followed complex pathways, largely attributed to the high virus infectivity, human travel patterns, and the implementation of multiple mitigation measures. The resulting geographic patterns describe the evolution of the epidemic and can indicate areas which are at risk of an outbreak. Here, we analyze the spatial correlations of new active cases in USA at the county level and characterize the extent of these correlations at different times. We show that the epidemic did not progress uniformly and we identify various stages which are distinguished by significant differences in the correlation length. Our results indicate that the correlation length may be large even during periods when the number of cases declines. We find that correlations between urban centers were much more significant than between rural areas and this finding indicates that long-range spreading was mainly facilitated by travel between cities, especially at the first months of the epidemic. We also show the existence of a percolation transition in November 2020, when the largest part of the country was connected to a spanning cluster, and a smaller-scale transition in January 2021, with both times corresponding to the peak of the epidemic in the country.

physics.soc-ph↗

arXiv2021

Cascading failures in anisotropic interdependent networks of spatial modular structures

The structure of real-world multilayer infrastructure systems usually exhibits anisotropy due to constraints of the embedding space. For example, geographical features like mountains, rivers and shores influence the architecture of critical infrastructure networks. Moreover, such spatial networks are often non-homogeneous but rather have a modular structure with dense connections within communities and sparse connections between neighboring communities. When the networks of the different layers are interdependent, local failures and attacks may propagate throughout the system. Here we study the robustness of spatial interdependent networks which are both anisotropic and heterogeneous. We also evaluate the effect of localized attacks having different geometrical shapes. We find that anisotropic networks are more robust against localized attacks and that anisotropic attacks, surprisingly, even on isotropic structures, are more effective than isotropic attacks.

physics.soc-ph↗