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Filippo Radicchi

Publications and source records attributed to Filippo Radicchi.

At least 91 records · Page 5Linked to original sources

Driving interconnected networks to supercriticality

Networks in the real world do not exist as isolated entities, but they are often part of more complicated structures composed of many interconnected network layers. Recent studies have shown that such mutual dependence makes real networked systems potentially exposed to atypical structural and dynamical behaviors, and thus there is a urgent necessity to better understand the mechanisms at the basis of these anomalies. Previous research has mainly focused on the emergence of atypical properties in relation with the moments of the intra- and inter-layer degree distributions. In this paper, we show that an additional ingredient plays a fundamental role for the possible scenario that an interconnected network can face: the correlation between intra- and inter-layer degrees. For sufficiently high amounts of correlation, an interconnected network can be tuned, by varying the moments of the intra- and inter-layer degree distributions, in distinct topological and dynamical regimes. When instead the correlation between intra- and inter-layer degrees is lower than a critical value, the system enters in a supercricritical regime where dynamical and topological phases are not longer distinguishable.

physics.soc-ph↗

Correlations between user voting data, budget, and box office for films in the Internet Movie Database

The Internet Movie Database (IMDb) is one of the most-visited websites in the world and the premier source for information on films. Like Wikipedia, much of IMDb's information is user contributed. IMDb also allows users to voice their opinion on the quality of films through voting. We investigate whether there is a connection between this user voting data and certain economic film characteristics. To this end, we perform distribution and correlation analysis on a set of films chosen to mitigate effects of bias due to the language and country of origin of films. We show that production budget, box office gross, and total number of user votes for films are consistent with double-log normal distributions for certain time periods. Both total gross and user votes are consistent with a double-log normal distribution from the late 1980s onward, while for budget, it extends from 1935 to 1979. In addition, we find a strong correlation between number of user votes and the economic statistics, particularly budget. Remarkably, we find no evidence for a correlation between number of votes and average user rating. As previous studies have found a strong correlation between production budget and marketing expenses, our results suggest that total user votes is an indicator of a film's prominence or notability, which can be quantified by its promotional costs.

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Universality of scholarly impact metrics

Given the growing use of impact metrics in the evaluation of scholars, journals, academic institutions, and even countries, there is a critical need for means to compare scientific impact across disciplinary boundaries. Unfortunately, citation-based metrics are strongly biased by diverse field sizes and publication and citation practices. As a result, we have witnessed an explosion in the number of newly proposed metrics that claim to be "universal." However, there is currently no way to objectively assess whether a normalized metric can actually compensate for disciplinary bias. We introduce a new method to assess the universality of any scholarly impact metric, and apply it to evaluate a number of established metrics. We also define a very simple new metric hs, which proves to be universal, thus allowing to compare the impact of scholars across scientific disciplines. These results move us closer to a formal methodology in the measure of scholarly impact.

cs.DL↗

Abrupt transition in the structural formation of interconnected networks

Our current world is linked by a complex mesh of networks where information, people and goods flow. These networks are interdependent each other, and present structural and dynamical features different from those observed in isolated networks. While examples of such "dissimilar" properties are becoming more abundant, for example diffusion, robustness and competition, it is not yet clear where these differences are rooted in. Here we show that the composition of independent networks into an interconnected network of networks undergoes a structurally sharp transition as the interconnections are formed. Depending of the relative importance of inter and intra-layer connections, we find that the entire interdependent system can be tuned between two regimes: in one regime, the various layers are structurally decoupled and they act as independent entities; in the other regime, network layers are indistinguishable and the whole system behave as a single-level network. We analytically show that the transition between the two regimes is discontinuous even for finite size networks. Thus, any real-world interconnected system is potentially at risk of abrupt changes in its structure that may reflect in new dynamical properties.

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Detectability of communities in heterogeneous networks

Communities are fundamental entities for the characterization of the structure of real networks. The standard approach to the identification of communities in networks is based on the optimization of a quality function known as "modularity". Although modularity has been at the center of an intense research activity and many methods for its maximization have been proposed, not much it is yet known about the necessary conditions that communities need to satisfy in order to be detectable with modularity maximization methods. Here, we develop a simple theory to establish these conditions, and we successfully apply it to various classes of network models. Our main result is that heterogeneity in the degree distribution helps modularity to correctly recover the community structure of a network and that, in the realistic case of scale-free networks with degree exponent $γ< 2.5$, modularity is always able to detect the presence of communities.

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Lévy flights in human behavior and cognition

Lévy flights represent the best strategy to randomly search for a target in an unknown environment, and have been widely observed in many animal species. Here, we inspect and discuss recent results concerning human behavior and cognition. Different studies have shown that human mobility can be described in terms of Lévy flights, while fresh evidence indicates that the same pattern accounts for human mental searches in online gambling sites. Thus, Lévy flights emerge as a unifying concept with broad cross-disciplinary implications. We argue that the ubiquity of such a pattern, both in behavior and cognition, suggests that the brain regions responsible for this behavior are likely to be evolutionarily old (i.e. no frontal cortex is involved), and that fMRI techniques might help to confirm this hypothesis.

physics.soc-ph↗

Analysis of bibliometric indicators for individual scholars in a large data set

Citation numbers and other quantities derived from bibliographic databases are becoming standard tools for the assessment of productivity and impact of research activities. Though widely used, still their statistical properties have not been well established so far. This is especially true in the case of bibliometric indicators aimed at the evaluation of individual scholars, because large-scale data sets are typically difficult to be retrieved. Here, we take advantage of a recently introduced large bibliographic data set, Google Scholar Citations, which collects the entire publication record of individual scholars. We analyze the scientific profile of more than 30,000 researchers, and study the relation between the h-index, the number of publications and the number of citations of individual scientists. While the number of publications of a scientist has a rather weak relation with his/her h-index, we find that the h-index of a scientist is strongly correlated with the number of citations that she/he has received so that the number of citations can be effectively be used as a proxy of the h-index. Allowing for the h-index to depend on both the number of citations and the number of publications, we find only a minor improvement.

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Field-normalized Impact Factors: A Comparison of Rescaling versus Fractionally Counted IFs

Two methods for comparing impact factors and citation rates across fields of science are tested against each other using citations to the 3,705 journals in the Science Citation Index 2010 (CD-Rom version of SCI) and the 13 field categories used for the Science and Engineering Indicators of the US National Science Board. We compare (i) normalization by counting citations in proportion to the length of the reference list (1/N of references) with (ii) rescaling by dividing citation scores by the arithmetic mean of the citation rate of the cluster. Rescaling is analytical and therefore independent of the quality of the attribution to the sets, whereas fractional counting provides an empirical strategy for normalization among sets (by evaluating the between-group variance). By the fairness test of Radicchi & Castellano (2012a), rescaling outperforms fractional counting of citations for reasons that we consider.

cs.DL↗

The Possible Role of Resource Requirements and Academic Career-Choice Risk on Gender Differences in Publication Rate and Impact

Many studies demonstrate that there is still a significant gender bias, especially at higher career levels, in many areas including science, technology, engineering, and mathematics (STEM). We investigated field-dependent, gender-specific effects of the selective pressures individuals experience as they pursue a career in academia within seven STEM disciplines. We built a unique database that comprises 437,787 publications authored by 4,292 faculty members at top United States research universities. Our analyses reveal that gender differences in publication rate and impact are discipline-specific. Our results also support two hypotheses. First, the widely-reported lower publication rates of female faculty are correlated with the amount of research resources typically needed in the discipline considered, and thus may be explained by the lower level of institutional support historically received by females. Second, in disciplines where pursuing an academic position incurs greater career risk, female faculty tend to have a greater fraction of higher impact publications than males. Our findings have significant, field-specific, policy implications for achieving diversity at the faculty level within the STEM disciplines.

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In science "there is no bad publicity": Papers criticized in comments have high scientific impact

Comments are special types of publications whose aim is to correct or criticize previously published papers. For this reason, comments are believed to make commented papers less worthy or trusty to the eyes of the scientific community, and thus predestined to have low scientific impact. Here, we show that such belief is not supported by empirical evidence. We consider thirteen major publication outlets in science, and perform systematic comparisons between the citations accumulated by commented and non commented articles. We find that (i) commented papers are, on average, much more cited than non commented papers, and (ii) commented papers are more likely to be among the most cited papers of a journal. Since comments are published soon after criticized papers, comments should be viewed as early indicators of the future impact of criticized papers.

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Universality, limits and predictability of gold-medal performances at the Olympic Games

Inspired by the Games held in ancient Greece, modern Olympics represent the world's largest pageant of athletic skill and competitive spirit. Performances of athletes at the Olympic Games mirror, since 1896, human potentialities in sports, and thus provide an optimal source of information for studying the evolution of sport achievements and predicting the limits that athletes can reach. Unfortunately, the models introduced so far for the description of athlete performances at the Olympics are either sophisticated or unrealistic, and more importantly, do not provide a unified theory for sport performances. Here, we address this issue by showing that relative performance improvements of medal winners at the Olympics are normally distributed, implying that the evolution of performance values can be described in good approximation as an exponential approach to an a priori unknown limiting performance value. This law holds for all specialties in athletics-including running, jumping, and throwing-and swimming. We present a self-consistent method, based on normality hypothesis testing, able to predict limiting performance values in all specialties. We further quantify the most likely years in which athletes will breach challenging performance walls in running, jumping, throwing, and swimming events, as well as the probability that new world records will be established at the next edition of the Olympic Games.

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Evolution of optimal Lévy-flight strategies in human mental searches

Recent analysis of empirical data [F. Radicchi, A. Baronchelli & L.A.N. Amaral. PloS ONE 7, e029910 (2012)] showed that humans adopt Lévy flight strategies when exploring the bid space in on-line auctions. A game theoretical model proved that the observed Lévy exponents are nearly optimal, being close to the exponent value that guarantees the maximal economical return to players. Here, we rationalize these findings by adopting an evolutionary perspective. We show that a simple evolutionary process is able to account for the empirical measurements with the only assumption that the reproductive fitness of a player is proportional to her search ability. Contrarily to previous modeling, our approach describes the emergence of the observed exponent without resorting to any strong assumptions on the initial searching strategies. Our results generalize earlier research, and open novel questions in cognitive, behavioral and evolutionary sciences.

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A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions

The large amount of information contained in bibliographic databases has recently boosted the use of citations, and other indicators based on citation numbers, as tools for the quantitative assessment of scientific research. Citations counts are often interpreted as proxies for the scientific influence of papers, journals, scholars, and institutions. However, a rigorous and scientifically grounded methodology for a correct use of citation counts is still missing. In particular, cross-disciplinary comparisons in terms of raw citation counts systematically favors scientific disciplines with higher citation and publication rates. Here we perform an exhaustive study of the citation patterns of millions of papers, and derive a simple transformation of citation counts able to suppress the disproportionate citation counts among scientific domains. We find that the transformation is well described by a power-law function, and that the parameter values of the transformation are typical features of each scientific discipline. Universal properties of citation patterns descend therefore from the fact that citation distributions for papers in a specific field are all part of the same family of univariate distributions.

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Rationality, irrationality and escalating behavior in lowest unique bid auctions

Information technology has revolutionized the traditional structure of markets. The removal of geographical and time constraints has fostered the growth of online auction markets, which now include millions of economic agents worldwide and annual transaction volumes in the billions of dollars. Here, we analyze bid histories of a little studied type of online auctions --- lowest unique bid auctions. Similarly to what has been reported for foraging animals searching for scarce food, we find that agents adopt Levy flight search strategies in their exploration of "bid space". The Levy regime, which is characterized by a power-law decaying probability distribution of step lengths, holds over nearly three orders of magnitude. We develop a quantitative model for lowest unique bid online auctions that reveals that agents use nearly optimal bidding strategies. However, agents participating in these auctions do not optimize their financial gain. Indeed, as long as there are many auction participants, a rational profit optimizing agent would choose not to participate in these auction markets.

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Testing the fairness of citation indicators for comparison across scientific domains: the case of fractional citation counts

Citation numbers are extensively used for assessing the quality of scientific research. The use of raw citation counts is generally misleading, especially when applied to cross-disciplinary comparisons, since the average number of citations received is strongly dependent on the scientific discipline of reference of the paper. Measuring and eliminating biases in citation patterns is crucial for a fair use of citation numbers. Several numerical indicators have been introduced with this aim, but so far a specific statistical test for estimating the fairness of these numerical indicators has not been developed. Here we present a statistical method aimed at estimating the effectiveness of numerical indicators in the suppression of citation biases. The method is simple to implement and can be easily generalized to various scenarios. As a practical example we test, in a controlled case, the fairness of fractional citation count, which has been recently proposed as a tool for cross-discipline comparison. We show that this indicator is not able to remove biases in citation patterns and performs much worse than the rescaling of citation counts with average values.

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Finding statistically significant communities in networks

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks.

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Rescaling citations of publications in physics

We analyze the citation distributions of all papers published in Physical Review journals between 1985 and 2009. The average number of citations received by papers published in a given year and in a given field is computed. Large variations are found, showing that it is not fair to compare citation numbers across fields and years. However, when a rescaling procedure by the average is used, it is possible to compare impartially articles across years and fields. We make the rescaling factors available for use by the readers. We also show that rescaling citation numbers by the number of publication authors has strong effects and should therefore be taken into account when assessing the bibliometric performance of researchers.

cs.DL↗

Information filtering in complex weighted networks

Many systems in nature, society and technology can be described as networks, where the vertices are the system's elements and edges between vertices indicate the interactions between the corresponding elements. Edges may be weighted if the interaction strength is measurable. However, the full network information is often redundant because tools and techniques from network analysis do not work or become very inefficient if the network is too dense and some weights may just reflect measurement errors, and shall be discarded. Moreover, since weight distributions in many complex weighted networks are broad, most of the weight is concentrated among a small fraction of all edges. It is then crucial to properly detect relevant edges. Simple thresholding would leave only the largest weights, disrupting the multiscale structure of the system, which is at the basis of the structure of complex networks, and ought to be kept. In this paper we propose a weight filtering technique based on a global null model (GloSS filter), keeping both the weight distribution and the full topological structure of the network. The method correctly quantifies the statistical significance of weights assigned independently to the edges from a given distribution. Applications to real networks reveal that the GloSS filter is indeed able to identify relevantconnections between vertices.

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