214 research outputs found
Power-Law Distributions and Binned Empirical data
No full textMany man-made and natural phenomenon, including the intensity of earthquakes, population of cities, and sizes of wars, are believed to follow power-law distributions, and the detection of these patterns has significant consequences for our understanding of the underlying mechanisms. However, the large fluctuations in the tail of these distributions makes it difficult to provide clear evidence for or against the power-law hypothesis, particularly when the empirical data have been binned. Clauset, Shalizi and Newman recently provided a statistically principled framework for identifying and testing power-law distributions in continuous or discrete valued data, based on maximum-likelihood fitting, goodness-of-fit test based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios for model comparison. We adapt these techniques to the less common but important case of binned empirical data. We evaluate the effectiveness of our techniques on synthetic data with known structure and apply them to ten real-world data sets with heavy-tailed patterns
Power-Law Distributions and Binned Empirical data
No full textMany man-made and natural phenomenon, including the intensity of earthquakes, population of cities, and sizes of wars, are believed to follow power-law distributions, and the detection of these patterns has significant consequences for our understanding of the underlying mechanisms. However, the large fluctuations in the tail of these distributions makes it difficult to provide clear evidence for or against the power-law hypothesis, particularly when the empirical data have been binned. Clauset, Shalizi and Newman recently provided a statistically principled framework for identifying and testing power-law distributions in continuous or discrete valued data, based on maximum-likelihood fitting, goodness-of-fit test based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios for model comparison. We adapt these techniques to the less common but important case of binned empirical data. We evaluate the effectiveness of our techniques on synthetic data with known structure and apply them to ten real-world data sets with heavy-tailed patterns
The Developmental Dynamics of Terrorist Organizations
No full textWe identify robust statistical patterns in the frequency and severity of violent attacks by terrorist organizations as they grow and age. Using group-level static and dynamic analyses of terrorist events worldwide from 1968-2008 and a simulation model of organizational dynamics, we show that the production of violent events tends to accelerate with increasing size and experience. This coupling of frequency, experience and size arises from a fundamental positive feedback loop in which attacks lead to growth which leads to increased production of new attacks. In contrast, event severity is independent of both size and experience. Thus larger, more experienced organizations are more deadly because they attack more frequently, not because their attacks are more deadly, and large events are equally likely to come from large and small organizations. These results hold across political ideologies and time, suggesting that the frequency and severity of terrorism may be constrained by fundamental processes. © 2012 Clauset, Gleditsch
How large should whales be?
No full textThe evolution and distribution of species body sizes for terrestrial mammals is well-explained by a macroevolutionary tradeoff between short-term selective advantages and long-term extinction risks from increased species body size, unfolding above the 2 g minimum size induced by thermoregulation in air. Here, we consider whether this same tradeoff, formalized as a constrained convection-reaction-diffusion system, can also explain the sizes of fully aquatic mammals, which have not previously been considered. By replacing the terrestrial minimum with a pelagic one, at roughly 7000 g, the terrestrial mammal tradeoff model accurately predicts, with no tunable parameters, the observed body masses of all extant cetacean species, including the 175,000,000 g Blue Whale. This strong agreement between theory and data suggests that a universal macroevolutionary tradeoff governs body size evolution for all mammals, regardless of their habitat. The dramatic sizes of cetaceans can thus be attributed mainly to the increased convective heat loss is water, which shifts the species size distribution upward and pushes its right tail into ranges inaccessible to terrestrial mammals. Under this macroevolutionary tradeoff, the largest expected species occurs where the rate at which smaller-bodied species move up into large-bodied niches approximately equals the rate at which extinction removes them
Hierarchical structure and the prediction of missing links in networks
No full textNetworks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science(1-3). Recent studies suggest that networks often exhibit hierarchical organization, in which vertices divide into groups that further subdivide into groups of groups, and so forth over multiple scales. In many cases the groups are found to correspond to known functional units, such as ecological niches in food webs, modules in biochemical networks ( protein interaction networks, metabolic networks or genetic regulatory networks) or communities in social networks(4-7). Here we present a general technique for inferring hierarchical structure from network data and show that the existence of hierarchy can simultaneously explain and quantitatively reproduce many commonly observed topological properties of networks, such as right- skewed degree distributions, high clustering coefficients and short path lengths. We further show that knowledge of hierarchical structure can be used to predict missing connections in partly known networks with high accuracy, and for more general network structures than competing techniques(8). Taken together, our results suggest that hierarchy is a central organizing principle of complex networks, capable of offering insight into many network phenomena.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62623/1/nature06830.pd
Trends and fluctuations in the severity of interstate wars
No full textAdvanced statistical models suggest that the likelihood of a large interstate war has not changed over 200 years.</jats:p
Untangling the network effects of productivity and prominence among scientists
No full text
While inequalities in science are common, most efforts to understand them treat scientists as isolated individuals, ignoring the network effects of collaboration. Here, we develop models that untangle the network effects of productivity defined as paper counts, and prominence referring to high-impact publications, of individual scientists from their collaboration networks. We find that gendered differences in the productivity and prominence of mid-career researchers can be largely explained by differences in their coauthorship networks. Hence, collaboration networks act as a form of social capital, and we find evidence of their transferability from senior to junior collaborators, with benefits that decay as researchers age. Collaboration network effects can also explain a large proportion of the productivity and prominence advantages held by researchers at prestigious institutions. These results highlight a substantial role of social networks in driving inequalities in science, and suggest that collaboration networks represent an important form of unequally distributed social capital that shapes who makes what scientific discoveries.
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