arXiv subjects

H. Eugene Stanley

Publications and source records attributed to H. Eugene Stanley.

At least 127 records · Page 7Linked to original sources

arXiv2011

Modeling Amphiphilic Solutes in a Jagla Solvent

Methanol is an amphiphilic solute whose aqueous solutions exhibit distinctive physical properties. The volume change upon mixing, for example, is negative across the entire composition range, indicating strong association. We explore the corresponding behavior of a Jagla solvent, which has been previously shown to exhibit many of the anomalous properties of water. We consider two models of an amphiphilic solute: (i) a "dimer" model, which consists of one hydrophobic hard sphere linked to a Jagla particle with a permanent bond, and (ii) a "monomer" model, which is a limiting case of the dimer, formed by concentrically overlapping a hard sphere and a Jagla particle. Using discrete molecular dynamics, we calculate the thermodynamic properties of the resulting solutions. We systematically vary the set of parameters of the dimer and monomer models and find that one can readily reproduce the experimental behavior of the excess volume of the methanolwater system as a function of methanol volume fraction. We compare the pressure and temperature dependence of the excess volume and the excess enthalpy of both models with experimental data on methanol-water solutions and find qualitative agreement in most cases. We also investigate the solute effect on the temperature of maximum density and find that the effect of concentration isorders of magnitude stronger than measured experimentally.

cond-mat.soft↗

arXiv2011

Scaling of Seismic Memory with Earthquake Size

It has been observed that the earthquake events possess short-term memory, i.e. that events occurring in a particular location are dependent on the short history of that location. We conduct an analysis to see whether real-time earthquake data also possess long-term memory and, if so, whether such autocorrelations depend on the size of earthquakes within close spatiotemporal proximity. We analyze the seismic waveform database recorded by 64 stations in Japan, including the 2011 "Great East Japan Earthquake", one of the five most powerful earthquakes ever recorded which resulted in a tsunami and devastating nuclear accidents. We explore the question of seismic memory through use of mean conditional intervals and detrended fluctuation analysis (DFA). We find that the waveform sign series show long-range power-law anticorrelations while the interval series show long-range power-law correlations. We find size-dependence in earthquake auto-correlations---as earthquake size increases, both of these correlation behaviors strengthen. We also find that the DFA scaling exponent $α$ has no dependence on earthquake hypocenter depth or epicentral distance.

physics.geo-ph↗

arXiv2011

Identification of influential spreaders in complex networks

Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases propagate within a society. Identifying the most efficient "spreaders" in a network is an important step to optimize the use of available resources and ensure the more efficient spread of information. Here we show that, in contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or to the most central people (high betweenness centrality). Instead, we find: (i) The most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis. (ii) When multiple spreaders are considered simultaneously, the distance between them becomes the crucial parameter that determines the extend of the spreading. Furthermore, we find that-- in the case of infections that do not confer immunity on recovered individuals-- the infection persists in the high k-shell layers of the network under conditions where hubs may not be able to preserve the infection. Our analysis provides a plausible route for an optimal design of efficient dissemination strategies.

physics.soc-ph↗

arXiv2011

Statistical regularities in the rank-citation profile of scientists

Recent "science of science" research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate scientific production and impact of individual careers using the rank-citation profile c_{i}(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each c_{i}(r) to a common distribution function that is parameterized by two scaling exponents. Since two scientists with equivalent Hirsch h-index can have significantly different c_{i}(r) profiles, our results demonstrate the utility of the β_{i} scaling parameter in conjunction with h_{i} for quantifying individual publication impact. We show that the total number of citations C_{i} tallied from a scientist's N_{i} papers scales as C_{i} \sim h_{i}^{1+β_{i}}. Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress.

physics.soc-ph↗

arXiv2011

Strategy of Competition between Two Groups based on a Contrarian Opinion Model

We introduce a contrarian opinion (CO) model in which a fraction p of contrarians within a group holds a strong opinion opposite to the opinion held by the rest of the group. At the initial stage, stable clusters of two opinions, A and B exist. Then we introduce contrarians which hold a strong B opinion into the opinion A group. Through their interactions, the contrarians are able to decrease the size of the largest A opinion cluster, and even destroy it. We see this kind of method in operation, e.g when companies send free new products to potential customers in order to convince them to adopt the product and influence others. We study the CO model, using two different strategies, on both ER and scale-free networks. In strategy I, the contrarians are positioned at random. In strategy II, the contrarians are chosen to be the highest degrees nodes. We find that for both strategies the size of the largest A cluster decreases to zero as p increases as in a phase transition. At a critical threshold value p_c the system undergoes a second-order phase transition that belongs to the same universality class of mean field percolation. We find that even for an ER type model, where the degrees of the nodes are not so distinct, strategy II is significantly more effctive in reducing the size of the largest A opinion cluster and, at very small values of p, the largest A opinion cluster is destroyed.

physics.data-an↗

arXiv2011

More than one dynamic crossover in protein hydration water

Studies of liquid water in its supercooled region have led to many insights into the structure and behavior of water. While bulk water freezes at its homogeneous nucleation temperature of approximately 235 K, for protein hydration water, the binding of water molecules to the protein avoids crystallization. Here we study the dynamics of the hydrogen bond (HB) network of a percolating layer of water molecules, comparing measurements of a hydrated globular protein with the results of a coarse-grained model that has been shown to successfully reproduce the properties of hydration water. With dielectric spectroscopy we measure the temperature dependence of the relaxation time of protons charge fluctuations. These fluctuations are associated to the dynamics of the HB network of water molecules adsorbed on the protein surface. With Monte Carlo (MC) simulations and mean--field (MF) calculations we study the dynamics and thermodynamics of the model. In both experimental and model analyses we find two dynamic crossovers: (i) one at about 252 K, and (ii) one at about 181 K. The agreement of the experiments with the model allows us to relate the two crossovers to the presence of two specific heat maxima at ambient pressure. The first is due to fluctuations in the HB formation, and the second, at lower temperature, is due to the cooperative reordering of the HB network.

cond-mat.soft↗

arXiv2011

Financial factor influence on scaling and memory of trading volume in stock market

We study the daily trading volume volatility of 17,197 stocks in the U.S. stock markets during the period 1989--2008 and analyze the time return intervals $τ$ between volume volatilities above a given threshold q. For different thresholds q, the probability density function P_q(τ) scales with mean interval <τ> as P_q(τ)=<τ>^{-1}f(τ/<τ>) and the tails of the scaling function can be well approximated by a power-law f(x)~x^{-γ}. We also study the relation between the form of the distribution function P_q(τ) and several financial factors: stock lifetime, market capitalization, volume, and trading value. We find a systematic tendency of P_q(τ) associated with these factors, suggesting a multi-scaling feature in the volume return intervals. We analyze the conditional probability P_q(τ|τ_0) for $τ$ following a certain interval τ_0, and find that P_q(τ|τ_0) depends on τ_0 such that immediately following a short/long return interval a second short/long return interval tends to occur. We also find indications that there is a long-term correlation in the daily volume volatility. We compare our results to those found earlier for price volatility.

q-fin.TR↗

arXiv2011

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: "For to all those who have, more will be given." Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew "rich get richer" effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his/her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect, and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals, and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience.

physics.pop-ph↗

arXiv2011

Detrending career statistics in professional baseball: Accounting for the steroids era and beyond

There is a long standing debate over how to objectively compare the career achievements of professional athletes from different historical eras. Developing an objective approach will be of particular importance over the next decade as Major League Baseball (MLB) players from the "steroids era" become eligible for Hall of Fame induction. Here we address this issue, as well as the general problem of comparing statistics from distinct eras, by detrending the seasonal statistics of professional baseball players. We detrend player statistics by normalizing achievements to seasonal averages, which accounts for changes in relative player ability resulting from both exogenous and endogenous factors, such as talent dilution from expansion, equipment and training improvements, as well as performance enhancing drugs (PED). In this paper we compare the probability density function (pdf) of detrended career statistics to the pdf of raw career statistics for five statistical categories -- hits (H), home runs (HR), runs batted in (RBI), wins (W) and strikeouts (K) -- over the 90-year period 1920-2009. We find that the functional form of these pdfs are stationary under detrending. This stationarity implies that the statistical regularity observed in the right-skewed distributions for longevity and success in professional baseball arises from both the wide range of intrinsic talent among athletes and the underlying nature of competition. Using this simple detrending technique, we examine the top 50 all-time careers for H, HR, RBI, W and K. We fit the pdfs for career success by the Gamma distribution in order to calculate objective benchmarks based on extreme statistics which can be used for the identification of extraordinary careers.

physics.soc-ph↗

arXiv2011

Quantifying and Modeling Long-Range Cross-Correlations in Multiple Time Series with Applications to World Stock Indices

We propose a modified time lag random matrix theory in order to study time lag cross-correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross-correlations in the absolute values of returns that quantify risk, and find that they decay much more slowly than cross-correlations between the returns. The magnitude of the cross-correlations constitute "bad news" for international investment managers who may believe that risk is reduced by diversifying across countries. We find that when a market shock is transmitted around the world, the risk decays very slowly. We explain these time lag cross-correlations by introducing a global factor model (GFM) in which all index returns fluctuate in response to a single global factor. For each pair of individual time series of returns, the cross-correlations between returns (or magnitudes) can be modeled with the auto-correlations of the global factor returns (or magnitudes). We estimate the global factor using principal component analysis, which minimizes the variance of the residuals after removing the global trend. Using random matrix theory, a significant fraction of the world index cross-correlations can be explained by the global factor, which supports the utility of the GFM. We demonstrate applications of the GFM in forecasting risks at the world level, and in finding uncorrelated individual indices. We find 10 indices are practically uncorrelated with the global factor and with the remainder of the world indices, which is relevant information for world managers in reducing their portfolio risk. Finally, we argue that this general method can be applied to a wide range of phenomena in which time series are measured, ranging from seismology and physiology to atmospheric geophysics.

q-fin.ST↗

arXiv2011

Large decrease of fluctuations for supercooled water in hydrophobic nanoconfinement

We study by Monte Carlo simulations a coarse-grained model of a water layer confined in a fixed disordered matrix of hydrophobic nanoparticles at different particle concentrations c. For c=0 (bulk water) we find a first order liquid-liquid phase transition (LLPT). For c>0 our simulations are consistent with a LLPT line ending in two critical points at low and high pressure P. For c=25% we find a dramatic decrease of compressibility K_T, thermal expansion coefficient α_P, specific heat C_P at high P and low temperature T. Surprisingly, the effect is present also for c as low as 2.4%. We conclude that the higher the confinement, the lower the thermodynamic fluctuations and the more difficult it will be to detect the LLPT.

cond-mat.stat-mech↗

arXiv2011

The Random Quadratic Assignment Problem

Optimal assignment of classes to classrooms \cite{dickey}, design of DNA microarrays \cite{carvalho}, cross species gene analysis \cite{kolar}, creation of hospital layouts cite{elshafei}, and assignment of components to locations on circuit boards \cite{steinberg} are a few of the many problems which have been formulated as a quadratic assignment problem (QAP). Originally formulated in 1957, the QAP is one of the most difficult of all combinatorial optimization problems. Here, we use statistical mechanical methods to study the asymptotic behavior of problems in which the entries of at least one of the two matrices that specify the problem are chosen from a random distribution $P$. Surprisingly, this case has not been studied before using statistical methods despite the fact that the QAP was first proposed over 50 years ago \cite{Koopmans}. We find simple forms for $C_{\rm min}$ and $C_{\rm max}$, the costs of the minimal and maximum solutions respectively. Notable features of our results are the symmetry of the results for $C_{\rm min}$ and $C_{\rm max}$ and the dependence on $P$ only through its mean and standard deviation, independent of the details of $P$. After the asymptotic cost is determined for a given QAP problem, one can straightforwardly calculate the asymptotic cost of a QAP problem specified with a different random distribution $P$.

cond-mat.stat-mech↗

arXiv2010

Robustness of interdependent networks under targeted attack

When an initial failure of nodes occurs in interdependent networks, a cascade of failure between the networks occurs. Earlier studies focused on random initial failures. Here we study the robustness of interdependent networks under targeted attack on high or low degree nodes. We introduce a general technique and show that the {\it targeted-attack} problem in interdependent networks can be mapped to the {\it random-attack} problem in a transformed pair of interdependent networks. We find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale free (SF) networks where the percolation threshold $p_c=0$, coupled SF networks are significantly more vulnerable with $p_c$ significantly larger than zero. The result implies that interdependent networks are difficult to defend by strategies such as protecting the high degree nodes that have been found useful to significantly improve robustness of single networks.

physics.soc-ph↗

arXiv2010

Cross-correlations between volume change and price change

In finance, one usually deals not with prices but with growth rates $R$, defined as the difference in logarithm between two consecutive prices. Here we consider not the trading volume, but rather the volume growth rate $\tilde R$, the difference in logarithm between two consecutive values of trading volume. To this end, we use several methods to analyze the properties of volume changes $|\tilde R|$, and their relationship to price changes $|R|$. We analyze $14,981$ daily recordings of the S\&P 500 index over the 59-year period 1950--2009, and find power-law {\it cross-correlations\/} between $|R|$ and $|\tilde R|$ using detrended cross-correlation analysis (DCCA). We introduce a joint stochastic process that models these cross-correlations. Motivated by the relationship between $| R|$ and $|\tilde R|$, we estimate the tail exponent ${\tildeα}$ of the probability density function $P(|\tilde R|) \sim |\tilde R|^{-1 -\tildeα}$ for both the S\&P 500 index as well as the collection of 1819 constituents of the New York Stock Exchange Composite index on 17 July 2009. As a new method to estimate $\tildeα$, we calculate the time intervals $τ_q$ between events where $\tilde R>q$. We demonstrate that $\barτ_q$, the average of $τ_q$, obeys $\bar τ_q \sim q^{\tildeα}$. We find $\tilde α\approx 3$. Furthermore, by aggregating all $τ_q$ values of 28 global financial indices, we also observe an approximate inverse cubic law.

q-fin.ST↗

arXiv2010

Bankruptcy risk model and empirical tests

We analyze the size dependence and temporal stability of firm bankruptcy risk in the US economy by applying Zipf scaling techniques. We focus on a single risk factor-the debt-to-asset ratio R-in order to study the stability of the Zipf distribution of R over time. We find that the Zipf exponent increases during market crashes, implying that firms go bankrupt with larger values of R. Based on the Zipf analysis, we employ Bayes's theorem and relate the conditional probability that a bankrupt firm has a ratio R with the conditional probability of bankruptcy for a firm with a given R value. For 2,737 bankrupt firms, we demonstrate size dependence in assets change during the bankruptcy proceedings. Prepetition firm assets and petition firm assets follow Zipf distributions but with different exponents, meaning that firms with smaller assets adjust their assets more than firms with larger assets during the bankruptcy process. We compare bankrupt firms with nonbankrupt firms by analyzing the assets and liabilities of two large subsets of the US economy: 2,545 Nasdaq members and 1,680 New York Stock Exchange (NYSE) members. We find that both assets and liabilities follow a Pareto distribution. The finding is not a trivial consequence of the Zipf scaling relationship of firm size quantified by employees-although the market capitalization of Nasdaq stocks follows a Pareto distribution, the same distribution does not describe NYSE stocks. We propose a coupled Simon model that simultaneously evolves both assets and debt with the possibility of bankruptcy, and we also consider the possibility of firm mergers.

q-fin.RM↗

arXiv2010

Cascade of failures in coupled network systems with multiple support-dependent relations

We study, both analytically and numerically, the cascade of failures in two coupled network systems A and B, where multiple support-dependent relations are randomly built between nodes of networks A and B. In our model we assume that each node in one network can function only if it has at least a single support node in the other network. If both networks A and B are Erdős-Rényi networks, A and B, with (i) sizes $N^A$ and $N^B$, (ii) average degrees $a$ and $b$, and (iii) $c^{AB}_0N^B$ support links from network A to B and $c^{BA}_0N^B$ support links from network B to A, we find that under random attack with removal of fractions $(1-R^A)N^A$ and $(1-R^B)N^B$ nodes respectively, the percolating giant components of both networks at the end of the cascading failures, $μ^A_\infty$ and $μ^B_\infty$, are given by the percolation laws $μ^A_\infty = R^A [1-\exp{({-c^{BA}_0μ^B_\infty})}] [1-\exp{({-aμ^A_\infty})}]$ and $μ^B_\infty = R^B [1-\exp{({-c^{AB}_0μ^A_\infty})}] [1-\exp{({-bμ^B_\infty})}]$. In the limit of $c^{BA}_0 \to \infty$ and $c^{AB}_0 \to \infty$, both networks become independent, and the giant components are equivalent to a random attack on a single Erdős-Rényi network. We also test our theory on two coupled scale-free networks, and find good agreement with the simulations.

physics.data-an↗

arXiv2010

Robustness of a Network of Networks

Almost all network research has been focused on the properties of a single network that does not interact and depends on other networks. In reality, many real-world networks interact with other networks. Here we develop an analytical framework for studying interacting networks and present an exact percolation law for a network of $n$ interdependent networks. In particular, we find that for $n$ Erdős-Rényi networks each of average degree $k$, the giant component, $P_{\infty}$, is given by $P_{\infty}=p[1-\exp(-kP_{\infty})]^n$ where $1-p$ is the initial fraction of removed nodes. Our general result coincides for $n=1$ with the known Erdős-Rényi second-order phase transition for a single network. For any $n \geq 2$ cascading failures occur and the transition becomes a first-order percolation transition. The new law for $P_{\infty}$ shows that percolation theory that is extensively studied in physics and mathematics is a limiting case ($n=1$) of a more general general and different percolation law for interdependent networks.

physics.data-an↗

arXiv2010

Statistical Properties of Cross-Correlation in the Korean Stock Market

We investigate the statistical properties of the correlation matrix between individual stocks traded in the Korean stock market using the random matrix theory (RMT) and observe how these affect the portfolio weights in the Markowitz portfolio theory. We find that the distribution of the correlation matrix is positively skewed and changes over time. We find that the eigenvalue distribution of original correlation matrix deviates from the eigenvalues predicted by the RMT, and the largest eigenvalue is 52 times larger than the maximum value among the eigenvalues predicted by the RMT. The $β_{473}$ coefficient, which reflect the largest eigenvalue property, is 0.8, while one of the eigenvalues in the RMT is approximately zero. Notably, we show that the entropy function $E(σ)$ with the portfolio risk $σ$ for the original and filtered correlation matrices are consistent with a power-law function, $E(σ) \sim σ^{-γ}$, with the exponent $γ\sim 2.92$ and those for Asian currency crisis decreases significantly.

q-fin.ST↗