Peter Klimek

Statistical detection of systematic election irregularities

Democratic societies are built around the principle of free and fair elections, and that each citizen’s vote should count equally. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies statistical consequences for the polling results, which can be used to identify election irregularities. Using a suitable data representation, we find that vote distributions of elections with alleged fraud show a kurtosis substantially exceeding the kurtosis of normal elections, depending on the level of data aggregation. As an example, we show that reported irregularities in recent Russian elections are, indeed, well-explained by systematic ballot stuffing. We develop a parametric model quantifying the extent to which fraudulent mechanisms are present. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces robust outcomes with respect to the resolution of the data and therefore, allows for cross-country comparisons.

Triadic closure dynamics drives scaling laws in social multiplex networks

Social networks exhibit scaling laws for several structural characteristics, such as degree distribution, scaling of the attachment kernel and clustering coefficients as a function of node degree. A detailed understanding if and how these scaling laws are inter-related is missing so far, let alone whether they can be understood through a common, dynamical principle. We propose a simple model for stationary network formation and show that the three mentioned scaling relations follow as natural consequences of triadic closure. The validity of the model is tested on multiplex data from a well-studied massive multiplayer online game. We find that the three scaling exponents observed in the multiplex data for the friendship, communication and trading networks can simultaneously be explained by the model. These results suggest that triadic closure could be identified as one of the fundamental dynamical principles in social multiplex network formation.

Quantification of excess risk for diabetes for those born in times of hunger, in an entire population of a nation, across a century

Based on a unique dataset comprising all 325,000 Austrian patients that were under pharmaceutical treatment for diabetes during 2006 and 2007, we measured the excess risk of developing diabetes triggered by undernourishment in early life. We studied the percentage of all diabetes patients in the total population specifically for each year of birth, from 1917 to 2007. We found a massive excess risk of diabetes in people born during the times of the three major famines and immediately after, which occurred in Austria in the 20th century: 1918–1919, 1938, and 1946–1947. Depending on the region, there was an up to 40% higher chance of having diabetes when born in 1919–1921, compared with 1918 or 1922, where age-specific typical diabetes ratios are observed. The excess risk for diabetes was practically absent in those provinces of Austria that were less affected by the famines. We show that diabetes rates exhibit nontrivial, age-specific sex differences, and correlate with the economic wealth of the region. Our results might be of relevance for establishing higher awareness in the health system for those born in high-risk years, and underline the importance of ensuring sufficient nutrition in prenatal and early stages of life.

Spreading of diseases through comorbidity networks across life and gender

The state of health of patients is typically not characterized by a single disease alone but by multiple (comorbid) medical conditions. These comorbidities may depend strongly on age and gender. We propose a specific phenomenological comorbidity network of human diseases that is based on medical claims data of the entire population of Austria. The network is constructed from a two-layer multiplex network, where in one layer the links represent the conditional probability for a comorbidity, and in the other the links contain the respective statistical significance. We show that the network undergoes dramatic structural changes across the lifetime of patients. Disease networks for children consist of a single, strongly interconnected cluster. During adolescence and adulthood further disease clusters emerge that are related to specific classes of diseases, such as circulatory, mental, or genitourinary disorders. For people over 65 these clusters start to merge, and highly connected hubs dominate the network. These hubs are related to hypertension, chronic ischemic heart diseases, and chronic obstructive pulmonary diseases. We introduce a simple diffusion model to understand the spreading of diseases on the disease network at the population level. For the first time we are able to show that patients predominantly develop diseases that are in close network proximity to disorders that they already suffer. The model explains more than 85% of the variance of all disease incidents in the population. The presented methodology could be of importance for anticipating age-dependent disease profiles for entire populations, and for design and validation of prevention strategies.

Circulating Betatrophin Is Strongly Increased in Pregnancy and Gestational Diabetes Mellitus.

Aims/hypothesis Betatrophin has recently been introduced as a novel hormone and promotor of beta cell proliferation and improved glucose tolerance in mouse models of insulin resistance. In obese and diabetic humans altered levels were reported and a role in pathophysiology of metabolic diseases was therefore hypothesized. However its release and regulation in women with gestational diabetes mellitus (GDM), as well as its associations with markers of obesity, glucose and lipid metabolism during pregnancy still remain unclear. Methods Circulating betatrophin was quantified in 21 women with GDM and 19 pregnant body mass index-matched women with normal glucose tolerance (NGT) as well as 10 healthy age-matched non-pregnant women by enzyme-linked immunosorbent assay. Additionally we performed radioimmunassay (RIA) to confirm the results. Results Betatrophin concentrations measured by ELISA were significantly higher in GDM than in NGT (29.3±4.4 ng/ml vs. 18.1±8.7 ng/ml, p<0.001) which was confirmed by RIA. Betatrophin did not correlate with BMI or insulin resistance but showed a weak association with leptin levels in pregnancy and negative relationship with fasting C-peptide levels in all women. Moreover it correlated significantly with lipid parameters including triglycerides and total cholesterol in pregnancy, as well as estrogen, progesteron and birth weight. Conclusions/interpretation Circulating betatrophin concentrations are dramatically increased in pregnancy and are significantly higher in GDM versus pregnant NGT. In the light of the previously reported role in lipid metabolism, betatrophin may represent a novel endocrine regulator of lipid alterations in pregnancy. However additional studies are needed to elucidate whether hormonal factors, such as estrogen, control the production of betatrophin and if targeting betatrophin could hold promise in the fight against metabolic disease.

Quantification of Diabetes Comorbidity Risks across Life Using Nation-Wide Big Claims Data

Despite substantial progress in the study of diabetes, important questions remain about its comorbidities and clinical heterogeneity. To explore these issues, we develop a framework allowing for the first time to quantify nation-wide risks and their age- and sex-dependence for each diabetic comorbidity, and whether the association may be consequential or causal, in a sample of almost two million patients. This study is equivalent to nearly 40,000 single clinical measurements. We confirm the highly controversial relation of increased risk for Parkinson’s disease in diabetics, using a 10 times larger cohort than previous studies on this relation. Detection of type 1 diabetes leads detection of depressions, whereas there is a strong comorbidity relation between type 2 diabetes and schizophrenia, suggesting similar pathogenic or medication-related mechanisms. We find significant sex differences in the progression of, for instance, sleep disorders and congestive heart failure in diabetic patients. Hypertension is a highly sex-sensitive comorbidity with females being at lower risk during fertile age, but at higher risk otherwise. These results may be useful to improve screening practices in the general population. Clinical management of diabetes must address age- and sex-dependence of multiple comorbid conditions.

Empirical Confirmation of Creative Destruction from World Trade Data

We show that world trade network datasets contain empirical evidence that the dynamics of innovation in the world economy indeed follows the concept of creative destruction, as proposed by J.A. Schumpeter more than half a century ago. National economies can be viewed as complex, evolving systems, driven by a stream of appearance and disappearance of goods and services. Products appear in bursts of creative cascades. We find that products systematically tend to co-appear, and that product appearances lead to massive disappearance events of existing products in the following years. The opposite–disappearances followed by periods of appearances–is not observed. This is an empirical validation of the dominance of cascading competitive replacement events on the scale of national economies, i.e., creative destruction. We find a tendency that more complex products drive out less complex ones, i.e., progress has a direction. Finally we show that the growth trajectory of a country’s product output diversity can be understood by a recently proposed evolutionary model of Schumpeterian economic dynamics.

Successful fish go with the flow: citation impact prediction based on centrality measures for term---document networks

In this work we address the challenge of how to identify those documents from a given set of texts that are most likely to have substantial impact in the future. To this end we develop a purely content-based methodology in order to rank a given set of documents, for example abstracts of scientific publications, according to their potential to generate impact as measured by the numbers of citations that the articles will receive in the future. We construct a bipartite network consisting of documents that are linked to keywords and terms that they contain. We study recursive centrality measures for such networks that quantify how many different terms a document contains and how these terms are related to each other. From this we derive a novel indicator—document centrality—that is shown to be highly predictive of citation impact in six different case studies. We compare these results to findings from a multivariable regression model and from conventional network-based centrality measures to show that document centrality indeed offers a comparably high performance in identifying those articles that contain a large number of high-impact keywords. Our findings suggest that articles which conform to the mainstream within a given research field tend to receive higher numbers of citations than highly original and innovative articles.

Dynamical origins of the community structure of an online multi-layer society

Social structures emerge as a result of individuals managing a variety of different social relationships. Societies can be represented as highly structured dynamic multiplex networks. Here we study the dynamical origins of the specific community structures of a large-scale social multiplex network of a human society that interacts in a virtual world of a massive multiplayer online game. There we find substantial differences in the community structures of different social actions, represented by the various layers in the multiplex network. Community sizes distributions are either fat-tailed or appear to be centered around a size of 50 individuals. To understand these observations we propose a voter model that is built around the principle of triadic closure. It explicitly models the co-evolution of node- and link-dynamics across different layers of the multiplex network. Depending on link and node fluctuation probabilities, the model exhibits an anomalous shattered fragmentation transition, where one layer fragments from one large component into many small components. The observed community size distributions are in good agreement with the predicted fragmentation in the model. This suggests that several detailed features of the fragmentation in societies can be traced back to the triadic closure processes.

Bibliometric analysis of fracking scientific literature

Abstract This study uses bibliometric methods to analyze the scientific literature of fracking. Web of Science database, including the Science Citation Index, Sciences Citation Index and Conference Proceedings Citation Index—Science were used to collect the data. The analysis done in the paper looks at the annual distribution of publications, countries, institutes, authors, journals and categories. Furthermore, key topics and highly-cited papers were analyzed. The results show that fracking as a new research term appears in the Web of Science records from 1953 and its presence in the Web of Science has been growing ever since, becoming a hot topic recently. The countries with most of the contributions have been USA, China and Canada, whereas the Russian Academy of Sciences, University of Oklahoma and Tohoku University were the three institutions with most publications in fracking research. The publications have been concentrated in several journals, led by the Journal of Petroleum Technology, Heфmянoe Xoзяйcmвo and International Journal of Rock Mechanics and Mining Sciences, and categorized mainly in geosciences multidisciplinary, Engineering Petroleum and Energy Fuels. The study has identified that terms of fracking can be divided into three main clusters, related to “drilling methods”, “exploitation/extraction process” and the “geoscience aspects”. The highly cited papers in the period 1953–2013 were collected and analyzed, in order to show the papers with highest impact in fracking area.