arXiv subjects

H. Eugene Stanley

Publications and source records attributed to H. Eugene Stanley.

At least 91 records · Page 5Linked to original sources

arXiv2014

Competing for Attention in Social Media under Information Overload Conditions

Although the many forms of modern social media have become major channels for the dissemination of information, they are becoming overloaded because of the rapidly-expanding number of information feeds. We analyze the expanding user-generated content in Sina Weibo, the largest micro-blog site in China, and find evidence that popular messages often follow a mechanism that differs from that found in the spread of disease, in contrast to common believe. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that in contrast to epidemics, for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her. Thus the greater the number of friends an individual has the greater the number of repeated contacts needed to spread the message, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold. This is in contrast to the zero threshold that conventional wisdom derives from disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received, leading to only a handful of information contents that can be widely spread throughout the population.

physics.soc-ph↗

arXiv2014

Reputation and Impact in Academic Careers

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here we develop an original framework for measuring how a publication's citation rate $Δc$ depends on the reputation of its central author $i$, in addition to its net citation count $c$. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly-cited scientists, using the total citations $C_{i}$ of each scientist as his/her reputation measure. We find a citation crossover $c_{\times}$ which distinguishes the strength of the reputation effect. For publications with $c < c_{\times}$, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in $C_{i}$. However, the reputation effect becomes negligible for highly cited publications meaning that for $c\geq c_{\times}$ the citation rate measures scientific impact more transparently. In addition we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.

physics.soc-ph↗

arXiv2014

Correlation between centrality metrics and their application to the opinion model

In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The m order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the B_{n}, the closeness, and the components of x_{1} are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is larger than that between x_{1} and a lower order degree mass. Finally, we investigate the effect of the inflexible antagonists selected based on different centrality metrics in helping one opinion to compete with another in the inflexible antagonists opinion model. Interestingly, we find that selecting the inflexible antagonists based on the leverage, the B_{n}, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the B_{n}, as well as a high centrality similarity between the leverage and the degree.

physics.soc-ph↗

arXiv2014

Ranking the Economic Importance of Countries and Industries

In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the world's economies, developing new effective approaches to risk evaluation, and providing mitigating solutions. We present a methodological framework for quantifying interdependencies in the global market and for evaluating risk levels in the world-wide financial network. The resulting information will enable policy and decision makers to better measure, understand, and maintain financial stability. We use the methodology to rank the economic importance of each industry and country according to the global damage that would result from their failure. Our quantitative results shed new light on China's increasing economic dominance over other economies, including that of the USA, to the global economy.

q-fin.GN↗

arXiv2014

Search for a liquid-liquid critical point in models of silica

Previous research has indicated the possible existence of a liquid-liquid critical point (LLCP) in models of silica at high pressure. To clarify this interesting question we run extended molecular dynamics simulations of two different silica models (WAC and BKS) and perform a detailed analysis of the liquid at temperatures much lower than those previously simulated. We find no LLCP in either model within the accessible temperature range, although it is closely approached in the case of the WAC potential near 4000 K and 5 GPa. Comparing our results with those obtained for other tetrahedral liquids, and relating the average Si-O-Si bond angle and liquid density at the model glass temperature to those of the ice-like beta-cristobalite structure, we conclude that the absence of a critical point can be attributed to insufficient "stiffness" in the bond angle. We hypothesize that a modification of the potential function to mildly favor larger average bond angles will generate a LLCP in a temperature range that is accessible to simulation. The tendency to crystallize in these models is extremely weak in the pressure range studied, although this tendency will undoubtedly increase with increasing stiffness.

cond-mat.soft↗

arXiv2014

Non-consensus opinion model on directed networks

Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectional links. Moreover, from choosing a coffee brand to deciding who to vote for in an election, two or more competing opinions often coexist. In response to this ubiquity of directed networks and the coexistence of two or more opinions in decision-making situations, we study a non-consensus opinion model introduced by Shao et al. \cite{shao2009dynamic} on directed networks. We define directionality $ξ$ as the percentage of unidirectional links in a network, and we use the linear correlation coefficient $ρ$ between the indegree and outdegree of a node to quantify the relation between the indegree and outdegree. We introduce two degree-preserving rewiring approaches which allow us to construct directed networks that can have a broad range of possible combinations of directionality $ξ$ and linear correlation coefficient $ρ$ and to study how $ξ$ and $ρ$ impact opinion competitions. We find that, as the directionality $ξ$ or the indegree and outdegree correlation $ρ$ increases, the majority opinion becomes more dominant and the minority opinion's ability to survive is lowered.

physics.soc-ph↗

arXiv2014

Predicting market instability: New dynamics between volume and volatility

Econophysics and econometrics agree that there is a correlation between volume and volatility in a time series. Using empirical data and their distributions, we further investigate this correlation and discover new ways that volatility and volume interact, particularly when the levels of both are high. We find that the distribution of the volume-conditional volatility is well fit by a power-law function with an exponential cutoff. We find that the volume-conditional volatility distribution scales with volume, and collapses these distributions to a single curve. We exploit the characteristics of the volume-volatility scatter plot to find a strong correlation between logarithmic volume and a quantity we define as local maximum volatility (LMV), which indicates the largest volatility observed in a given range of trading volumes. This finding supports our empirical analysis showing that volume is an excellent predictor of the maximum value of volatility for both same-day and near-future time periods. We also use a joint conditional probability that includes both volatility and volume to demonstrate that invoking both allows us to better predict the largest next-day volatility than invoking either one alone.

q-fin.ST↗

arXiv2014

Partial correlation analysis: Applications for financial markets

The presence of significant cross-correlations between the synchronous time evolution of a pair of equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate the level of similarity in the price changes for a given pair of stocks, but it does not measure whether other stocks influence the relationship between them. To explore the influence of a third stock on the relationship between two stocks, we use a partial correlation measurement to determine the underlying relationships between financial assets. Building on previous work, we present a statistically robust approach to extract the underlying relationships between stocks from four different financial markets: the United States, the United Kingdom, Japan, and India. This methodology provides new insights into financial market dynamics and uncovers implicit influences in play between stocks. To demonstrate the capabilities of this methodology, we (i) quantify the influence of different companies and, by studying market similarity across time, present new insights into market structure and market stability, and (ii) we present a practical application, which provides information on the how a company is influenced by different economic sectors, and how the sectors interact with each other. These examples demonstrate the effectiveness of this methodology in uncovering information valuable for a range of individuals, including not only investors and traders but also regulators and policy makers.

q-fin.ST↗

arXiv2014

Emergence of statistically validated financial intraday lead-lag relationships

According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology, however, markets have become more sophisticated. To determine whether this has resulted in an improved market efficiency, we investigate whether statistically significant lagged correlation relationships exist in financial markets. We introduce a numerical method to statistically validate links in correlation-based networks, and employ our method to study lagged correlation networks of equity returns in financial markets. Crucially, our statistical validation of lead-lag relationships accounts for multiple hypothesis testing over all stock pairs. In an analysis of intraday transaction data from the periods 2002--2003 and 2011--2012, we find a striking growth in the networks as we increase the frequency with which we sample returns. We compute how the number of validated links and the magnitude of correlations change with increasing sampling frequency, and compare the results between the two data sets. Finally, we compare topological properties of the directed correlation-based networks from the two periods using the in-degree and out-degree distributions and an analysis of three-node motifs. Our analysis suggests a growth in both the efficiency and instability of financial markets over the past decade.

q-fin.ST↗

arXiv2013

Robustness of Network of Networks with Interdependent and Interconnected links

Robustness of network of networks (NON) has been studied only for dependency coupling (J.X. Gao et. al., Nature Physics, 2012) and only for connectivity coupling (E.A. Leicht and R.M. D Souza, arxiv:0907.0894). The case of network of n networks with both interdependent and interconnected links is more complicated, and also more closely to real-life coupled network systems. Here we develop a framework to study analytically and numerically the robustness of this system. For the case of starlike network of n ER networks, we find that the system undergoes from second order to first order phase transition as coupling strength q increases. We find that increasing intra-connectivity links or inter-connectivity links can increase the robustness of the system, while the interdependency links decrease its robustness. Especially when q=1, we find exact analytical solutions of the giant component and the first order transition point. Understanding the robustness of network of networks with interdependent and interconnected links is helpful to design resilient infrastructures.

physics.soc-ph↗

arXiv2013

Percolation on interacting networks with feedback-dependency links

When real networks are considered, coupled networks with connectivity and feedback-dependency links are not rare but more general. Here we develop a mathematical framework and study numerically and analytically percolation of interacting networks with feedback-dependency links. We find that when nodes of between networks are lowly connected, the system undergoes from second order transition through hybrid order transition to first order transition as coupling strength increases. And, as average degree of each inter-network increases, first order region becomes smaller and second-order region becomes larger but hybrid order region almost keep constant. Especially, the results implies that average degree \bar{k} between intra-networks has a little influence on robustness of system for weak coupling strength, but for strong coupling strength corresponding to first order transition system become robust as \bar{k} increases. However, when average degree k of inter-network is increased, the system become robust for all coupling strength. Additionally, when nodes of between networks are highly connected, the hybrid order region disappears and the system first order region becomes larger and secondorder region becomes smaller. Moreover, we find that the existence of feedback dependency links between interconnecting networks makes the system extremely vulnerable by comparing non-feedback condition for the same parameters.

physics.soc-ph↗

arXiv2013

Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets

In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST network whose metric distance is defined in terms of cross-correlation coefficient absolute values, enabling the connections between anticorrelated entities to manifest properly. We investigate 69 daily time series, comprising three types of financial assets: 28 stock market indicators, 21 currency futures, and 20 commodity futures. We show that though the resulting MST network evolves over time, the financial assets of similar type tend to have connections which are stable over time. In addition, we find a characteristic time lag between the volatility time series of the stock market indicators and those of the EU CO2 emission allowance (EUA) and crude oil futures (WTI). This time lag is given by the peak of the cross-correlation function of the volatility time series EUA (or WTI) with that of the stock market indicators, and is markedly different (>20 days) from 0, showing that the volatility of stock market indicators today can predict the volatility of EU emissions allowances and of crude oil in the near future.

q-fin.ST↗

arXiv2013

Robustness of partially interdependent network formed of clustered networks

Clustering, or transitivity has been observed in real networks and its effects on their structure and function has been discussed extensively. The focus of these studies has been on clustering of single networks while the effect of clustering on the robustness of coupled networks received very little attention. Only the case of a pair of fully coupled networks with clustering has been studied recently. Here we generalize the study of clustering of a fully coupled pair of networks to the study of partially interdependent network of networks with clustering within the network components. We show both analytically and numerically, how clustering within the networks, affects the percolation properties of interdependent networks, including percolation threshold, size of giant component and critical coupling point where first order phase transition changes to second order phase transition as the coupling between the networks reduces. We study two types of clustering: one type proposed by Newman where the average degree is kept constant while changing the clustering and the other proposed by Hackett $et$ $al.$ where the degree distribution is kept constant. The first type of clustering is treated both analytically and numerically while the second one is treated only numerically.

physics.soc-ph↗

arXiv2013

Percolation of a general network of networks

Percolation theory is an approach to study vulnerability of a system. We develop analytical framework and analyze percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of $n$ classic Erdős-Rényi (ER) networks, where each network depends on the same number $m$ of other networks, i.e., a random regular network of interdependent ER networks. In contrast to a \emph{treelike} NetONet in which the size of the largest connected cluster (mutual component) depends on $n$, the loops in the RR NetONet cause the largest connected cluster to depend only on $m$. We also analyzed the extremely vulnerable feedback condition of coupling. In the case of ER networks, the NetONet only exhibits two phases, a second order phase transition and collapse, and there is no first phase transition regime unlike the no feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when $q$ is large and second order phase transition when $q$ is small. Our results can help in designing robust interdependent systems.

physics.soc-ph↗

arXiv2013

Systemic risk and spatiotemporal dynamics of the US housing market

Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify rich economic information in the largest eigenvalues deviating from RMT predictions and unveil that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. Our results show that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. Our analysis uncovers that dramatic increases in the systemic risk are usually accompanied with regime shifts, which provides a means of early detection of housing bubbles.

q-fin.ST↗

arXiv2013

Effect of the Interconnected Network Structure on the Epidemic Threshold

Most real-world networks are not isolated. In order to function fully, they are interconnected with other networks, and this interconnection influences their dynamic processes. For example, when the spread of a disease involves two species, the dynamics of the spread within each species (the contact network) differs from that of the spread between the two species (the interconnected network). We model two generic interconnected networks using two adjacency matrices, A and B, in which A is a 2N*2N matrix that depicts the connectivity within each of two networks of size N, and B a 2N*2N matrix that depicts the interconnections between the two. Using an N-intertwined mean-field approximation, we determine that a critical susceptable-infected-susceptable (SIS) epidemic threshold in two interconnected networks is 1/λ1(A+αB), where the infection rate is βwithin each of the two individual networks and αβin the interconnected links between the two networks and λ1(A+αB) is the largest eigenvalue of the matrix A+αB. In order to determine how the epidemic threshold is dependent upon the structure of interconnected networks, we analytically derive λ1(A+αB) using perturbation approximation for small and large α, the lower and upper bound for any αas a function of the adjacency matrix of the two individual networks, and the interconnections between the two and their largest eigenvalues/eigenvectors. We verify these approximation and boundary values for λ1(A+αB) using numerical simulations, and determine how component network features affect λ1(A+αB).

physics.soc-ph↗

arXiv2013

Percolation of Partially Interdependent Scale-free Networks

We study the percolation behavior of two interdependent scale-free (SF) networks under random failure of 1-$p$ fraction of nodes. Our results are based on numerical solutions of analytical expressions and simulations. We find that as the coupling strength between the two networks $q$ reduces from 1 (fully coupled) to 0 (no coupling), there exist two critical coupling strengths $q_1$ and $q_2$, which separate three different regions with different behavior of the giant component as a function of $p$. (i) For $q \geq q_1$, an abrupt collapse transition occurs at $p=p_c$. (ii) For $q_2 3$, $q_1$ decreases with increasing $λ$. $(b)$ In the hybrid transition, at the $q_2 < q < q_1$ region, the mutual giant component $P_{\infty}$ jumps discontinuously at $p=p^{jump}_c$ to a very small but non-zero value, and when reducing $p$, $P_{\infty}$ continuously approaches to 0 at $p_c = 0$ for $λ< 3$ and at $p_c > 0$ for $λ> 3$. Thus, the known theoretical $p_c=0$ for a single network with $λ\leqslant 3$ is expected to be valid also for strictly partial interdependent networks.

physics.data-an↗

arXiv2013

Finite-size scaling investigation of the liquid-liquid critical point in ST2 water and its stability with respect to crystallization

The liquid-liquid critical point scenario of water hypothesizes the existence of two metastable liquid phases---low-density liquid (LDL) and high-density liquid (HDL)---deep within the supercooled region. The hypothesis originates from computer simulations of the ST2 water model, but the stability of the LDL phase with respect to the crystal is still being debated. We simulate supercooled ST2 water at constant pressure, constant temperature and constant number of molecules N for N<=729 and times up to 1000 ns. We observe clear differences between the two liquids, both structural and dynamical. Using several methods, including finite-size scaling, we confirm the presence of a liquid-liquid phase transition ending in a critical point. We find that the LDL is stable with respect to the crystal in 98% of our runs (we perform 372 runs for LDL or LDL-like states), and in 100% of our runs for the two largest system sizes (N=512 and 729, for which we perform 136 runs for LDL or LDL-like states). In all these runs tiny crystallites grow and then melt within 1000 ns. Only for N<=343 we observe six events (over 236 runs for LDL or LDL-like states) of spontaneous crystallization after crystallites reach an estimated critical size of about 70+/-10 molecules.

cond-mat.soft↗