IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T07:05:35ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2015-11-02T14:20:02Z2015-11-02T14:20:02Zhttp://eprints.imtlucca.it/id/eprint/2801This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28012015-11-02T14:20:02ZQuantifying randomness in real networksRepresented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks—the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain—and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.Chiara OrsiniMarija M. DankulovPol Colomer-de-SimónAlmerima JamakovicPriya MahadevanAmin VahdatKevin E. BasslerZoltán ToroczkaiMarián BoguñáGuido Caldarelliguido.caldarelli@imtlucca.itSanto FortunatoDmitri Krioukov2014-12-02T15:39:26Z2014-12-18T13:55:36Zhttp://eprints.imtlucca.it/id/eprint/2386This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23862014-12-02T15:39:26ZCharacterizing and modeling citation dynamicsCitation distributions are crucial for the analysis and modeling of the activity of scientists. We investigated bibliometric data of papers published in journals of the American Physical Society, searching for the type of function which best describes the observed citation distributions. We used the goodness of fit with Kolmogorov-Smirnov statistics for three classes of functions: log-normal, simple power law and shifted power law. The shifted power law turns out to be the most reliable hypothesis for all citation networks we derived, which correspond to different time spans. We find that citation dynamics is characterized by bursts, usually occurring within a few years since publication of a paper, and the burst size spans several orders of magnitude. We also investigated the microscopic mechanisms for the evolution of citation networks, by proposing a linear preferential attachment with time dependent initial attractiveness. The model successfully reproduces the empirical citation distributions and accounts for the presence of citation bursts as well.Young-Ho Eomyoungho.eom@imtlucca.itSanto Fortunato2014-12-02T15:33:39Z2014-12-18T13:56:05Zhttp://eprints.imtlucca.it/id/eprint/2385This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23852014-12-02T15:33:39ZHow citation boosts promote scientific paradigm shifts and Nobel PrizesNobel Prizes are commonly seen to be among the most prestigious achievements of our times. Based on mining several million citations, we quantitatively analyze the processes driving paradigm shifts in science. We find that groundbreaking discoveries of Nobel Prize Laureates and other famous scientists are not only acknowledged by many citations of their landmark papers. Surprisingly, they also boost the citation rates of their previous publications. Given that innovations must outcompete the rich-gets-richer effect for scientific citations, it turns out that they can make their way only through citation cascades. A quantitative analysis reveals how and why they happen. Science appears to behave like a self-organized critical system, in which citation cascades of all sizes occur, from continuous scientific progress all the way up to scientific revolutions, which change the way we see our world. Measuring the “boosting effect” of landmark papers, our analysis reveals how new ideas and new players can make their way and finally triumph in a world dominated by established paradigms. The underlying “boost factor” is also useful to discover scientific breakthroughs and talents much earlier than through classical citation analysis, which by now has become a widespread method to measure scientific excellence, influencing scientific careers and the distribution of research funds. Our findings reveal patterns of collective social behavior, which are also interesting from an attention economics perspective. Understanding the origin of scientific authority may therefore ultimately help to explain how social influence comes about and why the value of goods depends so strongly on the attention they attract.Amin MazloumianYoung-Ho Eomyoungho.eom@imtlucca.itDirk HelbingSergi LozanoSanto Fortunato2014-11-17T11:38:14Z2014-11-17T11:38:14Zhttp://eprints.imtlucca.it/id/eprint/2370This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23702014-11-17T11:38:14ZReputation and impact in academic careersReputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication’s citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, the author’s reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science. Alexander M. Petersenalexander.petersen@imtlucca.itSanto FortunatoRaj K. PanKimmo KaskiOrion Pennerorion.penner@imtlucca.itArmando Rungiarmando.rungi@imtlucca.itMassimo Riccabonimassimo.riccaboni@imtlucca.itH. Eugene StanleyFabio Pammollif.pammolli@imtlucca.it2014-07-02T10:54:06Z2014-07-02T10:54:06Zhttp://eprints.imtlucca.it/id/eprint/2231This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22312014-07-02T10:54:06ZOn the Predictability of Future Impact in ScienceCorrectly assessing a scientist’s past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate’s future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist’s future impact. By applying that future impact model to 762 careers drawn from three disciplines: physics, biology, and mathematics, we identify a number of subtle, but critical, flaws in current models. Specifically, cumulative non-decreasing measures like the h-index contain intrinsic autocorrelation, resulting in significant overestimation of their ‘‘predictive power’’. Moreover, the predictive power of these models depend heavily upon scientists’ career age, producing least accurate estimates for young researchers. Our results place in doubt the suitability of such models, and indicate further investigation is required before they can be used in recruiting decisions.Orion Pennerorion.penner@imtlucca.itRaj K. PanAlexander M. Petersenalexander.petersen@imtlucca.itKimmo KaskiSanto Fortunato2013-04-09T10:04:01Z2013-04-09T10:04:01Zhttp://eprints.imtlucca.it/id/eprint/1540This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/15402013-04-09T10:04:01ZCommentary: The case for caution in predicting scientists’ future impactOrion Pennerorion.penner@imtlucca.itAlexander M. Petersenalexander.petersen@imtlucca.itRaj K. PanSanto Fortunato