Growing complex network of citations of scientific papers: Modeling and measurements.

We consider the network of citations of scientific papers and use a combination of the theoretical and experimental tools to uncover microscopic details of this network growth. Namely, we develop a stochastic model of citation dynamics based on the copying-redirection-triadic closure mechanism. In a complementary and coherent way, the model accounts both for statistics of references of scientific papers and for their citation dynamics. Originating in empirical measurements, the model is cast in such a way that it can be verified quantitatively in every aspect. Such validation is performed by measuring citation dynamics of physics papers. The measurements revealed nonlinear citation dynamics, the nonlinearity being intricately related to network topology. The nonlinearity has far-reaching consequences including nonstationary citation distributions, diverging citation trajectories of similar papers, runaways or "immortal papers" with infinite citation lifetime, etc. Thus nonlinearity in complex network growth is our most important finding. In a more specific context, our results can be a basis for quantitative probabilistic prediction of citation dynamics of individual papers and of the journal impact factor.

Species survival emerge from rare events of individual migration.

Ecosystems greatly vary in their species composition and interactions, yet they all show remarkable resilience to external influences. Recent experiments have highlighted the significant effects of spatial structure and connectivity on the extinction and survival of species. It has also been emphasized lately that in order to study extinction dynamics reliably, it is essential to incorporate stochasticity, and in particular the discrete nature of populations, into the model. Accordingly, we applied a bottom-up modeling approach that includes both spatial features and stochastic interactions to study survival mechanisms of species. Using the simplest spatial extension of the Lotka-Volterra predator-prey model with competition, subject to demographic and environmental noise, we were able to systematically study emergent properties of this rich system. By scanning the relevant parameter space, we show that both survival and extinction processes often result from a combination of habitat fragmentation and individual rare events of recolonization.

Short and long term investor synchronization caused by decoupling.

The dynamics of collective decision making is not yet well understood. Its practical relevance however can be of utmost importance, as experienced by people who lost their fortunes in turbulent moments of financial markets. In this paper we show how spontaneous collective "moods" or "biases" emerge dynamically among human participants playing a trading game in a simple model of the stock market. Applying theory and computer simulations to the experimental data generated by humans, we are able to predict the onset of such moments before they actually happen.

Stochastic dynamical model of a growing citation network based on a self-exciting point process.

We put under experimental scrutiny the preferential attachment model that is commonly accepted as a generating mechanism of the scale-free complex networks. To this end we chose a citation network of physics papers and traced the citation history of 40,195 papers published in one year. Contrary to common belief, we find that the citation dynamics of the individual papers follows the superlinear preferential attachment, with the exponent α=1.25-1.3. Moreover, we show that the citation process cannot be described as a memoryless Markov chain since there is a substantial correlation between the present and recent citation rates of a paper. Based on our findings we construct a stochastic growth model of the citation network, perform numerical simulations based on this model and achieve an excellent agreement with the measured citation distributions.

Big words, halved brains and small worlds: complex brain networks of figurative language comprehension.

Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.

The 'intrinsic' system in the human cortex and self-projection: a data driven analysis.

Information received by the human cortex is supplied by two main sources: extrinsic stimuli delivered by the external environment and intrinsic information regarding the body and self. We reanalyzed electrophysiological data involving the same external stimuli, but manipulating the degree of 'self-projection' to locations inside and outside the body border. Electrical neuroimaging and spatial principal component analysis (PCA) showed a bipartition of the cerebral cortex into two main subsystems: occipital and frontal activity was similar across tasks; activity in temporo-parietal and anterior frontal regions was modulated according to the manipulation of self-projection in a given task. These data suggest that the first system relates to external stimulus processing ('extrinsic') and the latter one relates to processing of the 'internal milieu' of body and self ('intrinsic').

Self-emergence of knowledge trees: extraction of the Wikipedia hierarchies.

The rapid accumulation of knowledge and the recent emergence of new dynamic and practically unmoderated information repositories have rendered the classical concept of the hierarchal knowledge structure irrelevant and impossible to impose manually. This led to modern methods of data location, such as browsing or searching, which conceal the underlying information structure. We here propose methods designed to automatically construct a hierarchy from a network of related terms. We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit. We verify our methods in two networks with no a priori hierarchy (the E. Coli genetic regulatory network and the C. Elegans neural network) and a network of function libraries of modern computer operating systems that are intrinsically hierarchical and reproduce a known functional order.

Copying nodes versus editing links: the source of the difference between genetic regulatory networks and the WWW.

UNLABELLED: We study two kinds of networks: genetic regulatory networks and the World Wide Web. We systematically test microscopic mechanisms to find the set of such mechanisms that optimally explain each networks' specific properties. In the first case we formulate a model including mainly random unbiased gene duplications and mutations. In the second case, the basic moves are website generation and rapid surf-induced link creation (/destruction). The different types of mechanisms reproduce the appropriate observed network properties. We use those to show that different kinds of networks have strongly system-dependent macroscopic experimental features. The diverging properties result from dissimilar node and link basic dynamics. The main non-uniform properties include the clustering coefficient, small-scale motifs frequency, time correlations, centrality and the connectivity of outgoing links. Some other features are generic such as the large-scale connectivity distribution of incoming links (scale-free) and the network diameter (small-worlds). The common properties are just the general hallmark of autocatalysis (self-enhancing processes), while the specific properties hinge on the specific elementary mechanisms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

Antigen-receptor degeneracy and immunological paradigms.

This paper discusses some consequences of the discovery that antigen receptors are degenerate: Immune specificity, in contrast to the tenets of the clonal selection paradigm, must be generated by the immune response down-stream of initial antigen recognition; and specificity is a property of a collective of cells and not of single clones.

Proliferation and competition in discrete biological systems.

We study the emergence of collective spatio-temporal objects in biological systems by representing individually the elementary interactions between their microscopic components. We use the immune system as a prototype for such interactions. The results of this detailed explicit analysis are compared with the traditional procedure of representing the collective dynamics in terms of densities that obey partial differential equations. The simulations show even for very simple elementary reactions the spontaneous emergence of localized complex structures, from microscopic noise. In turn the effective dynamics of these structures affects the average behaviour of the system in a very decisive way: systems which would according to the differential equations approximation die, display in reality a very lively behaviour. As the optimal modelling method we propose a mixture of microscopic simulation systems describing each reaction separately, and continuous methods describing the average behaviour of the agents.

World-size global markets lead to economic instability.

Economic and cultural globalization is one of the most important processes humankind has been undergoing lately. This process is assumed to be leading the world into a wealthy society with a better life. However, the current trend of globalization is not unprecedented in human history, and has had some severe consequences in the past. By applying a quantitative analysis through a microscopic representation we show that globalization, besides being unfair (with respect to wealth distribution), is also unstable and potentially dangerous as one event may lead to a collapse of the system. It is proposed that the optimal solution in controlling the unwanted aspects and enhancing the advantageous ones lies in limiting competition to large subregions, rather than making it worldwide.

Theoretical analysis and simulations of the generalized Lotka-Volterra model.

The dynamics of generalized Lotka-Volterra systems is studied by theoretical techniques and computer simulations. These systems describe the time evolution of the wealth distribution of individuals in a society, as well as of the market values of firms in the stock market. The individual wealths or market values are given by a set of time dependent variables w(i), i=1,...,N. The equations include a stochastic autocatalytic term (representing investments), a drift term (representing social security payments), and a time dependent saturation term (due to the finite size of the economy). The w(i)'s turn out to exhibit a power-law distribution of the form P(w) approximately w(-1-alpha). It is shown analytically that the exponent alpha can be expressed as a function of one parameter, which is the ratio between the constant drift component (social security) and the fluctuating component (investments). This result provides a link between the lower and upper cutoffs of this distribution, namely, between the resources available to the poorest and those available to the richest in a given society. The value of alpha is found to be insensitive to variations in the saturation term, which represent the expansion or contraction of the economy. The results are of much relevance to empirical studies that show that the distribution of the individual wealth in different countries during different periods in the 20th century has followed a power-law distribution with 1