Complex networks of real-world systems are believed to be controlled by common phenomena, producing structures far from regular or random. These include scale-free degree distributions, small-world structure and assortative mixing by degree, which are also the properties captured by different random graph models proposed in the literature. However, many (non-social) real-world networks are in fact disassortative by degree. Thus, we here propose a simple evolving model that generates networks with most common properties of real-world networks including degree disassortativity. Furthermore, the model has a natural interpretation for citation networks with different practical applications. [more]

In most social, information, and collaboration systems the complex activity of agents generates rapidly evolving time-varying networks. Temporal changes in the network structure and the dynamical processes occurring on its fabric are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset describing the activity of millions of individuals and investigate the temporal evolution of their egocentric networks. We empirically observe a simple statistical law characterizing the memory of agents that quantitatively signals how much interactions are more likely to happen again on already established connections. We encode the observed dynamics in a reinforcement process defining a generative computational network model with time-varying connectivity patterns. This activity-driven network model spontaneously generates the basic dynamic process for the differentiation between strong and weak ties. The model is used to study the effect of time-varying heterogeneous interactions on the spreading of information on social networks. We observe that the presence of strong ties may severely inhibit the large scale spreading of information by confining the process among agents with recurrent communication patterns. Our results provide the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks. [more]

We present work in jointly inferring the unique individuals as well as their social rank within a collection of letters from an Old Assyrian trade colony in K\"ultepe, Turkey, settled by merchants from the ancient city of Assur for approximately 200 years between 1950-1750 BCE, the height of the Middle Bronze Age. Using a probabilistic latent-variable model, we leverage pairwise social differences between names in cuneiform tablets to infer a single underlying social order that best explains the data we observe. Evaluating our output with published judgments by domain experts suggests that our method may be used for building informed hypotheses that are driven by data, and that may offer promising avenues for directed research by Assyriologists. [more]

Prices in financial markets exhibit extreme jumps far more often than can be accounted for by external news. Further, magnitudes of price changes are correlated over long times. These so called stylized facts are quantified by scaling laws similar to, for example, turbulent fluids. They are believed to reflect the complex interactions of heterogenous agents which give rise to irrational herding. Therefore, the stylized facts have been argued to provide evidence against the efficient market hypothesis which states that prices rapidly reflect available information and therefore are described by a martingale. Here we show, that in very simple bidding processes efficiency is not opposed to, but causative to scaling properties observed in real markets. Thereby, we link the stylized facts not only to price efficiency, but also to the economic theory of rational bubbles. We then demonstrate effects predicted from our normative model in the dynamics of groups of real human subjects playing a modified minority game. An extended version of the latter can be played online at seesaw.neuro.uni-bremen.de. [more]

The average economic agent is often used to model the dynamics of simple markets, based on the assumption that the dynamics of many agents can be averaged over in time and space. A popular idea that is based on this seemingly intuitive notion is to dampen electric power fluctuations from fluctuating sources (as e.g. wind or solar) via a market mechanism, namely by variable power prices that adapt demand to supply. The standard model of an average economic agent predicts that fluctuations are reduced by such an adaptive pricing mechanism. <br />However, the underlying assumption that the actions of all agents average out on the time axis is not always true in a market of many agents. We numerically study an econophysics agent model of an adaptive power market that does not assume averaging a priori. We find that when agents are exposed to source noise via correlated price fluctuations (as adaptive pricing schemes suggest), the market may amplify those fluctuations. In particular, small price changes may translate to large load fluctuations through catastrophic consumer synchronization. As a result, an adaptive power market may cause the opposite effect than intended: Power fluctuations are not dampened but amplified instead. [more]

Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across different countries and see a global centralization of light. Observing 160 different countries we document the expansion of developing countries, the growth of new agglomerations, the regression in countries suffering from demographic decline and the success of light pollution abatement programs in western countries. [more]

The cusp catastrophe theory has been primarily developed as a deterministic theory for systems that may respond to continuous changes in a control variables by a discontinuous change. While most of the systems in behavioral sciences are subject to noise, and in behavioral finance moreover to time-varying volatility, it may be difficult to apply the theory in these fields. This paper addresses the issue and proposes a two-step estimation methodology, which will allow us to apply the catastrophe theory to model stock market crashes. Utilizing high frequency data, we estimate the daily realized volatility from the returns in the first step and use the stochastic cusp catastrophe on the data normalized by the estimated volatility in the second step to study possible discontinuities in markets. We support our methodology by simulations where we also discuss the importance of stochastic noise and volatility in the deterministic cusp model. The methodology is empirically tested on almost 27 years of U.S. stock market evolution covering several important recessions and crisis periods. Results suggest that the proposed methodology provides an important shift in application of catastrophe theory to stock markets. We show that stock markets subject to noise and time-varying volatility shows strong bifurcation marks. Due to the very long sample period we also develop a rolling estimation approach, where we study the dynamics of the parameters and we find that while in the first half of the period stock markets showed strong marks of bifurcations, in the second half catastrophe theory was not able to confirm this behavior. Results may have an important implications for understanding the recent deep financial crisis of 2008. [more]

We introduce a tractable multi-currency model with stochastic volatility and correlated stochastic interest rates that takes into account the smile in the FX market and the evolution of yield curves. The pricing of vanilla options on FX rates can be performed effciently through the FFT methodology thanks to the affinity of the model Our framework is also able to describe many non trivial links between FX rates and interest rates: a second calibration exercise highlights the ability of the model to fit simultaneously FX implied volatilities while being coherent with interest rate products. [more]

Social organization and division of labor crucially influence the performance of collaborative software engineering efforts. In this paper, we provide a quantitative analysis of the relation between social organization and performance in Gentoo, an Open Source community developing a Linux distribution. We study the structure and dynamics of collaborations as recorded in the project's bug tracking system over a period of ten years. We identify a period of increasing centralization after which most interactions in the community were mediated by a single central contributor. In this period of maximum centralization, the central contributor unexpectedly left the project, thus posing a significant challenge for the community. We quantify how the rise, the activity as well as the subsequent sudden dropout of this central contributor affected both the social organization and the bug handling performance of the Gentoo community. We analyze social organization from the perspective of network theory and augment our quantitative findings by interviews with prominent members of the Gentoo community which shared their personal insights. [more]

Air transport is a key infrastructure of modern societies. In this paper we review some recent approaches to air transport, which make extensive use of theory of complex networks. We discuss possible networks that can be defined for the air transport and we focus our attention to networks of airports connected by flights. We review several papers investigating the topology of these networks and their dynamics for time scales ranging from years to intraday intervals, and consider also the resilience properties of air networks to extreme events. Finally we discuss the results of some recent papers investigating the dynamics on air transport network, with emphasis on passengers traveling in the network and epidemic spreading mediated by air transport. [more]