arXiv subjects

Shlomo Havlin

Publications and source records attributed to Shlomo Havlin.

At least 73 records · Page 4Linked to original sources

arXiv2021

Concurrence Percolation in Quantum Networks

Establishing long-distance quantum entanglement, i.e., entanglement transmission, in quantum networks (QN) is a key and timely challenge for developing efficient quantum communication. Traditional comprehension based on classical percolation assumes a necessary condition for successful entanglement transmission between any two infinitely distant nodes: they must be connected by at least a path of perfectly entangled states (singlets). Here, we relax this condition by explicitly showing that one can focus not on optimally converting singlets but on establishing concurrence -- a key measure of bipartite entanglement. We thereby introduce a new statistical theory, concurrence percolation theory (ConPT), remotely analogous to classical percolation but fundamentally different, built by generalizing bond percolation in terms of "sponge-crossing" paths instead of clusters. Inspired by resistance network analysis, we determine the path connectivity by series/parallel rules and approximate higher-order rules via star-mesh transforms. Interestingly, we find that the entanglement transmission threshold predicted by ConPT is lower than the known classical-percolation-based results and is readily achievable on any series-parallel networks such as the Bethe lattice. ConPT promotes our understanding of how well quantum communication can be further systematically improved versus classical statistical predictions under the limitation of QN locality -- a "quantum advantage" that is more general and efficient than expected. ConPT also shows a percolation-like universal critical behavior derived by finite-size analysis on the Bethe lattice and regular two-dimensional lattices, offering new perspectives for a theory of criticality in entanglement statistics.

quant-ph↗

arXiv2020

Network Geometry

Real networks are finite metric spaces. Yet the geometry induced by shortest path distances in a network is definitely not its only geometry. Other forms of network geometry are the geometry of latent spaces underlying many networks, and the effective geometry induced by dynamical processes in networks. These three approaches to network geometry are all intimately related, and all three of them have been found to be exceptionally efficient in discovering fractality, scale-invariance, self-similarity, and other forms of fundamental symmetries in networks. Network geometry is also of great utility in a variety of practical applications, ranging from the understanding how the brain works, to routing in the Internet. Here, we review the most important theoretical and practical developments dealing with these approaches to network geometry in the last two decades, and offer perspectives on future research directions and challenges in this novel frontier in the study of complexity.

physics.soc-ph↗

arXiv2020

Optimal COVID-19 infection spread under low temperature, dry air, and low UV radiation

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, is currently spreading rapidly throughout the world, causing many deaths and severe economic damage. It is believed that hot and humid conditions do not favor the novel coronavirus, yet this is still under debate due to many uncertainties associated with the COVID-19 data. Here we propose surrogate data tests to examine the preference of this virus to spread under different climate conditions. We find that the disease is significantly (above the 95% confidence level) more common when the temperature is $\sim$10$^\circ$C, the specific humidity is $\sim$5 g/kg, and the ultraviolet (UV) radiation is $\sim$80 kJ/m$^2$. The significance of relative humidity is below the 95% confidence level and does not show a preferred value. The results are supported using global and regional data, spanning the time period from January to July 2020. The COVID-19 data includes the daily reported new cases and daily death cases; for both, the population size is either taken into account or ignored.

physics.med-ph↗

arXiv2020

Globalization emergence in the European Patent Office (EPO) patent network

We studied the evolution of the European Patent Office (EPO) patents applicants' collaborations network, within a 35 years span of data (1978-2013). Focusing on the Giant Component (GC) formation process over many time-windows, distributed throughout the data timeline, we found that the features governing this phenomenon are indicative of emerging globalization in the applicants' collaborations. The timeline appears to be divided into three regimes, corresponding to three states of the network's evolution. In the early years state, the GC takes long to form and the instant of its creation is easily pinpointed, while it features geographically segregated groups of applicants with technologically similar activities. In contrast, in the late years state, the GC forms quickly, the exact point of its creation is harder to spot, the applicants' activities are more disparate technologically, while their inter-regional collaborations are significantly increased. The middle years are an intermediate state between the two extreme of early and late years. Moreover, we concluded that the critical patents, which induce the GC's formation, are typically introduced by large-sized applicants and also that top patent-producing applicants are likely to submit critical patents, albeit at a lower rate than their overall patent submission. Lastly, we uncovered the crucial role that Japan plays in the network's coherence, through its prominent participation in the GC and the critical patents.

physics.soc-ph↗

arXiv2020

Epidemic spreading and control strategies in spatial modular network

Epidemic spread on networks is one of the most studied dynamics in network science and has important implications in real epidemic scenarios. Nonetheless, the dynamics of real epidemics and how it is affected by the underline structure of the infection channels are still not fully understood. Here we apply the SIR model and study analytically and numerically the epidemic spread on a recently developed spatial modular model imitating the structure of cities in a country. The model assumes that inside a city the infection channels connect many different locations, while the infection channels between cities are less and usually directly connect only a few nearest neighbor cities in a two-dimensional plane. We find that the model experience two epidemic transitions. The first lower threshold represents a local epidemic spread within a city but not to the entire country and the second higher threshold represents a global epidemic in the entire country. Based on our analytical solution we proposed several control strategies and how to optimize them. We also show that while control strategies can successfully control the disease, early actions are essentials to prevent the disease global spread.

physics.soc-ph↗

arXiv2020

Statistical physics approaches to the complex Earth system

Global climate change, extreme climate events, earthquakes and their accompanying natural disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, strategic interactions and complex structure of the Earth system, the understanding and in particular the predicting of such disruptive events represent formidable challenges for both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge for a better understanding of the Earth system, including climate extreme events, earthquakes and Earth geometric relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as, critical phenomena, network theory, percolation, tipping points analysis, as well as entropy can be applied to complex Earth systems (climate, earthquakes, etc.). Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics approaches can be useful in the field of Earth system science.

physics.soc-ph↗

arXiv2020

Interdependent transport via percolation backbones in spatial networks

The functionality of nodes in a network is often described by the structural feature of belonging to the giant component. However, when dealing with problems like transport, a more appropriate functionality criterion is for a node to belong to the network's backbone, where the flow of information and of other physical quantities (such as current) occurs. Here we study percolation in a model of interdependent resistor networks and show the effect of spatiality on their coupled functioning. We do this on a realistic model of spatial networks, featuring a Poisson distribution of link-lengths. We find that interdependent resistor networks are significantly more vulnerable than their percolation-based counterparts, featuring first-order phase transitions at link-lengths where the mutual giant component still emerges continuously. We explain this apparent contradiction by tracing the origin of the increased vulnerability of interdependent transport to the crucial role played by the dandling ends. Moreover, we interpret these differences by considering an heterogeneous $k$-core percolation process which enables to define a one-parameter family of functionality criteria whose constraints become more and more stringent. Our results highlight the importance that different definitions of nodes functionality have on the collective properties of coupled processes, and provide better understanding of the problem of interdependent transport in many real-world networks.

physics.soc-ph↗

arXiv2020

Cascading Failures in Complex Networks

Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems, and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behavior. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.

physics.soc-ph↗

arXiv2020

Geometric characterization of SARS-CoV-2 pandemic events

While the SARS-CoV-2 keeps spreading world-wide, comparing its evolution across different nations is a timely challenge of both theoretical and practical importance. The large variety of dissimilar and country-dependent epidemiological factors, in fact, makes extremely difficult to understand their influence on the epidemic trends within a unique and coherent framework. We present a geometric framework to characterize, in an integrated and low-dimensional fashion, the epidemic plume-like trajectories traced by the infection rate, $I$, and the fatality rate, $D$, in the $(I,D)$ plane. Our analysis enables the definition of an epidemiometric system based on three geometric observables rating the SARS-CoV-2 pandemic events via scales analogous to those for the magnitude and the intensity of seismic events. Being exquisitely geometric, our framework can be applied to classify other epidemic data and secondary waves, raising the possibility of designing epidemic alerts or early warning systems to enhance public and governmental responses to a rapidly emerging outbreak.

physics.soc-ph↗

arXiv2020

The critical role of fresh teams in creating original and multi-disciplinary research

Teamwork is one of the most prominent features in modern science. It is now well-understood that the team size is an important factor that affects team creativity. However, the crucial question of how the character of research studies is influenced by the freshness of the team remains unclear. In this paper, we quantify the team freshness according to the absent of prior collaboration among team members. Our results suggest that fresher teams tend to produce works of higher originality and more multi-disciplinary impact. These effects are even magnified in larger teams. Furthermore, we find that freshness defined by new team members in a paper is a more effective indicator of research originality and multi-disciplinarity compared to freshness defined by new collaboration relations among team members. Finally, we show that career freshness of members also plays an important role in increasing the originality and multi-disciplinarity of produced papers.

physics.soc-ph↗

arXiv2020

Percolation framework reveals limits of privacy in Conspiracy, Dark Web, and Blockchain networks

We consider the privacy of interactions between individuals in a network. For many networks, while nodes are anonymous to outside observers, the existence of a link between individuals implies the possibility of one node revealing identifying information about its neighbor. Moreover, while the identities of the accounts are likely hidden to an observer, the network of interaction between two anonymous accounts is often available. For example, in blockchain cryptocurrencies, transactions between two anonymous accounts are published openly. Here we consider what happens if one (or more) parties in such a network are deanonymized by an outside identity. These compromised individuals could leak information about others with whom they interacted, which could then cascade to more and more nodes' information being revealed. We use a percolation framework to analyze the scenario outlined above and show for different likelihoods of individuals possessing information on their counter-parties, the fraction of accounts that can be identified and the idealized minimum number of steps from a deanonymized node to an anonymous node (a measure of the effort required to deanonymize that individual). We further develop a greedy algorithm to estimate the \emph{actual} number of steps that will be needed to identify a particular node based on the noisy information available to the attacker. We apply our framework to three real-world networks: (1) a blockchain transaction network, (2) a network of interactions on the dark web, and (3) a political conspiracy network. We find that in all three networks, beginning from one compromised individual, it is possible to deanonymize a significant fraction of the network ($>50$%) within less than 5 steps. Overall these results provide guidelines for investigators seeking to identify actors in anonymous networks, as well as for users seeking to maintain their privacy.

physics.soc-ph↗

arXiv2020

Predictability of real temporal networks

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidences have shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here we propose an entropy-rate based framework, based on combined topological-temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological-temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension the combined topological-temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity of incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.

cs.SI↗

arXiv2020

Spatio-temporal propagation of COVID-19 pandemics

The new coronavirus known as COVID-19 is spread world-wide since December 2019. Without any vaccination or medicine, the means of controlling it are limited to quarantine and social distancing. Here we study the spatio-temporal propagation of the first wave of the COVID-19 virus in China and compare it to other global locations. We provide a comprehensive picture of the spatial propagation from Hubei to other provinces in China in terms of distance, population size, and human mobility and their scaling relations. Since strict quarantine has been usually applied between cities, more insight about the temporal evolution of the disease can be obtained by analyzing the epidemic within cities, especially the time evolution of the infection, death, and recovery rates which affected by policies. We study and compare the infection rate in different cities in China and provinces in Italy and find that the disease spread is characterized by a two-stages process. At early times, at order of few days, the infection rate is close to a constant probably due to the lack of means to detect infected individuals before infection symptoms are observed. Then at later times it decays approximately exponentially due to quarantines. The time evolution of the death and recovery rates also distinguish between these two stages and reflect the health system situation which could be overloaded.

physics.soc-ph↗

arXiv2020

Efficient network immunization under limited knowledge

Targeted immunization or attacks of large-scale networks has attracted significant attention by the scientific community. However, in real-world scenarios, knowledge and observations of the network may be limited thereby precluding a full assessment of the optimal nodes to immunize (or remove) in order to avoid epidemic spreading such as that of current COVID-19 epidemic. Here, we study a novel immunization strategy where only $n$ nodes are observed at a time and the most central between these $n$ nodes is immunized (or attacked). This process is continued repeatedly until $1-p$ fraction of nodes are immunized (or attacked). We develop an analytical framework for this approach and determine the critical percolation threshold $p_c$ and the size of the giant component $P_{\infty}$ for networks with arbitrary degree distributions $P(k)$. In the limit of $n\to\infty$ we recover prior work on targeted attack, whereas for $n=1$ we recover the known case of random failure. Between these two extremes, we observe that as $n$ increases, $p_c$ increases quickly towards its optimal value under targeted immunization (attack) with complete information. In particular, we find a new scaling relationship between $|p_c(\infty)-p_c(n)|$ and $n$ as $|p_c(\infty)-p_c(n)|\sim n^{-1}\exp(-αn)$. For Scale-free (SF) networks, where $P(k)\sim k^{-γ}, 2<γ<3$, we find that $p_c$ has a transition from zero to non-zero when $n$ increases from $n=1$ to order of $\log N$ ($N$ is the size of network). Thus, for SF networks, knowledge of order of $\log N$ nodes and immunizing them can reduce dramatically an epidemics.

physics.soc-ph↗

arXiv2020

Improved Earthquake Forecasting Model Based on Long-term Memory in Earthquake

A prominent feature of earthquakes is their empirical laws including memory (clustering) in time and space. Several earthquake forecasting models, like the EpidemicType Aftershock Sequence (ETAS) model, were developed based on earthquake empirical laws. Yet, a recent study showed that the ETAS model fails in reproducing significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) recently discovered in the data. Our generalized ETAS model reproduces accurately the short- and long-term/distance memory observed in the Italian and South California earthquake catalogs. The revised ETAS model is also found to significantly improve earthquake forecasting.

physics.geo-ph↗

arXiv2020

Very early warning signal for El Niño in 2020 with a 4 in 5 likelihood

The El Niño Southern Oscillation (ENSO) is the most important driver of climate variability and can trigger extreme weather events and disasters in various parts of the globe. Recently we have developed a network approach, which allows forecasting an El Niño event about 1 year ahead. Here we communicate that since 2012 this network approach, which does not involve any fit parameter, correctly predicted the absence of El Niño events in 2012, 2013 and 2017 as well as the onset of the large El Niño event that started in 2014 and ended in 2016. Our model also correctly forecasted the onset of the last El Niño event in 2018. In September 2019, the model indicated the return of El Niño in 2020 with an 80% probability.

physics.ao-ph↗

arXiv2020

Two transitions in spatial modular networks

Understanding the resilience of infrastructures such as transportation network has significant importance for our daily life. Recently, a homogeneous spatial network model was developed for studying spatial embedded networks with characteristic link length such as power-grids and the brain. However, although many real-world networks are spatially embedded and their links have characteristics length such as pipelines, power lines or ground transportation lines they are not homogeneous but rather heterogeneous. For example, density of links within cities are significantly higher than between cities. Here we present and study numerically and analytically a similar realistic heterogeneous spatial modular model using percolation process to better understand the effect of heterogeneity on such networks. The model assumes that inside a city there are many lines connecting different locations, while long lines between the cities are sparse and usually directly connecting only a few nearest neighbours cities in a two dimensional plane. We find that this model experiences two distinct continues transitions, one when the cities disconnect from each other and the second when each city breaks apart. Although the critical threshold for site percolation in 2D grid remains an open question we analytically find the critical threshold for site percolation in this model. In addition, while the homogeneous model experience a single transition having a unique phenomenon called \textit{critical stretching} where a geometric crossover from random to spatial structure in different scales found to stretch non-linearly with the characteristic length at criticality. Here we show that the heterogeneous model does not experience such a phenomenon indicating that critical stretching strongly depends on the network structure.

physics.soc-ph↗

arXiv2020

Discrimination universally determines reconstruction of multiplex networks

Network reconstruction is fundamental to understanding the dynamical behaviors of the networked systems. Many systems, modeled by multiplex networks with various types of interactions, display an entirely different dynamical behavior compared to the corresponding aggregated network. In many cases, unfortunately, only the aggregated topology and partial observations of the network layers are available, raising an urgent demand for reconstructing multiplex networks. We fill this gap by developing a mathematical and computational tool based on the Expectation-Maximization framework to reconstruct multiplex layer structures. The reconstruction accuracy depends on the various factors, such as partial observation and network characteristics, limiting our ability to predict and allocate observations. Surprisingly, by using a mean-field approximation, we discovered that a discrimination indicator that integrates all these factors universally determines the accuracy of reconstruction. This discovery enables us to design the optimal strategies to allocate the fixed budget for deriving the partial observations, promoting the optimal reconstruction of multiplex networks. To further evaluate the performance of our method, we predict beside structure also dynamical behaviors on the multiplex networks, including percolation, random walk, and spreading processes. Finally, applying our method on empirical multiplex networks drawn from biological, transportation, and social domains, corroborate the theoretical analysis.

physics.soc-ph↗