Impact, productivity, and scientific excellence

Citation metrics are becoming pervasive in the quantitative evaluation of scholars, journals and institutions. More then ever before, hiring, promotion, and funding decisions rely on a variety of impact metrics that cannot disentangle quality from productivity, and are biased by factors such as discipline and academic age. Biases affecting the evaluation of single papers are compounded when one aggregates citation-based metrics across an entire publication record. It is not trivial to compare the quality of two scholars that during their careers have published at different rates in different disciplines in different periods of time. We propose a novel solution based on the generation of a statistical baseline specifically tailored on the academic profile of each researcher. By decoupling productivity and impact, our method can determine whether a certain level of impact can be explained by productivity alone, or additional ingredients of scientific excellence are necessary. The method is flexible enough to allow for the evaluation of, and fair comparison among, arbitrary collections of papers --- scholar publication records, journals, and entire institutions; and can be extended to simultaneously suppresses any source of bias. We show that our method can capture the quality of the work of Nobel laureates irrespective of productivity, academic age, and discipline, even when traditional metrics indicate low impact in absolute terms. We further apply our methodology to almost a million scholars and over six thousand journals to quantify the impact required to demonstrate scientific excellence for a given level of productivity.

Impact, productivity, and scientific excellence
Jasleen Kaur, Emilio Ferrara, FilippoMenczer, Alessandro Flammini, FilippoRadicchi

http://arxiv.org/abs/1411.7357

Inheritance Patterns in Citation Networks Reveal Scientific Memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Inheritance Patterns in Citation Networks Reveal Scientific Memes
Phys. Rev. X 4, 041036 – Published 21 November 2014
Tobias Kuhn, MatjažPerc, and Dirk Helbing

http://dx.doi.org/10.1103/PhysRevX.4.041036

Predicting scientific success based on coauthorship networks

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100,000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a Machine Learning classifier, based only on coauthorship network centrality metrics measured at the time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing – challenging the perception of citations as an objective, socially unbiased measure of scientific success.

Predicting scientific success based on coauthorship networks
EmreSarigöl, René Pfitzner, Ingo Scholtes, AntoniosGaras and Frank Schweitzer

EPJ Data Science 2014, 3:9  http://dx.doi.org/10.1140/epjds/s13688-014-0009-x

Modeling social dynamics in a collaborative environment

Wikipedia is a prime example of today’s value production in a collaborative environment. Using this example, we model the emergence, persistence and resolution of severe conflicts during collaboration by coupling opinion formation with article editing in a bounded confidence dynamics. The complex social behavior involved in editing articles is implemented as a minimal model with two basic elements; (i) individuals interact directly to share information and convince each other, and (ii) they edit a common medium to establish their own opinions. Opinions of the editors and that represented by the article are characterised by a scalar variable. When the pool of editors is fixed, three regimes can be distinguished: (a) a stable mainstream article opinion is continuously contested by editors with extremist views and there is slow convergence towards consensus, (b) the article oscillates between editors with extremist views, reaching consensus relatively fast at one of the extremes, and (c) the extremist editors are converted very fast to the mainstream opinion and the article has an erratic evolution. When editors are renewed with a certain rate, a dynamical transition occurs between different kinds of edit wars, which qualitatively reflect the dynamics of conflicts as observed in real Wikipedia data.

Modeling social dynamics in a collaborative environment
Gerardo Iñiguez, JánosTörök, TahaYasseri, KimmoKaski and JánosKertész

EPJ Data Science 2014, 3:7  http://dx.doi.org/10.1140/epjds/s13688-014-0007-z

Social media for large studies of behavior

CONCLUSIONS. The biases and issues highlighted above will not affect all research in the same way. Well-reasoned judgment on the part of authors, reviewers, and editors is warranted here. Many of the issues discussed have well-known solutions contributed by other fields such as epidemiology, statistics, and machine learning. In some cases, the solutions are difficult to fit with practical realities (e.g., as in the case of proper significance testing) whereas in other cases the community simply has not broadly adopted best practices (e.g., independent data sets for testing machine learning techniques) or the existing solutions may be subject to biases of their own. Regardless, a crucial step is to resolve the disconnect that exists between this research community and other (often related) fields with methods and practices for managing analytical bias.

Social media for large studies of behavior
Derek Ruths, Jürgen Pfeffer

Science 28 November 2014: 
Vol. 346 no. 6213 pp. 1063-1064 
http://dx.doi.org/10.1126/science.346.6213.1063

Evolutionary dynamics of time-resolved social interactions

Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics. Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.

Evolutionary dynamics of time-resolved social interactions
Phys. Rev. E 90, 052825 – Published 25 November 2014
AlessioCardillo, Giovanni Petri, Vincenzo Nicosia, Roberta Sinatra, Jesús Gómez-Gardeñes, and Vito Latora

http://dx.doi.org/10.1103/PhysRevE.90.052825

The big data debate

“Big data”—the collection, aggregation or federation, and analysis of vast amounts of increasingly granular data—present serious challenges not only to personal privacy but also to the tools we use to protect it. Privacy, Big Data, and the Public Good focuses valuable attention on two of these tools: notice and consent, and de-identification—the process of preventing a person's identity from being linked to specific data. The book presents a collection of essays from a variety of perspectives, in chapters by some of the heavy hitters in the privacy debate, who make a convincing case that the current framework for dealing with consumer privacy does not adequately address issues posed by big data.

The big data debate
Fred H. Cate
Privacy, Big Data, and the Public Good Frameworks for Engagement Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum, Eds. Cambridge University Press, 2014. 342 pp.

Science 14 November 2014: 
Vol. 346 no. 6211 p. 818 
http://dx.doi.org/10.1126/science.1261092

Spatial patterns of close relationships across the lifespan

The dynamics of close relationships is important for understanding the migration patterns of individual life-courses. The bottom-up approach to this subject by social scientists has been limited by sample size, while the more recent top-down approach using large-scale datasets suffers from a lack of detail about the human individuals. We incorporate the geographic and demographic information of millions of mobile phone users with their communication patterns to study the dynamics of close relationships and its effect in their life-course migration. We demonstrate how the close age- and sex-biased dyadic relationships are correlated with the geographic proximity of the pair of individuals, e.g., young couples tend to live further from each other than old couples. In addition, we find that emotionally closer pairs are living geographically closer to each other. These findings imply that the life-course framework is crucial for understanding the complex dynamics of close relationships and their effect on the migration patterns of human individuals.

Spatial patterns of close relationships across the lifespan
• Hang-Hyun Jo, JariSaramäki, Robin I. M. Dunbar &KimmoKaski

Scientific Reports 4, Article number: 6988 http://dx.doi.org/10.1038/srep06988

Reputation and impact in academic careers

Over a scientist’s career, a reputation is developed, a standing within a research community, based largely upon the quantity and quality of his/her publications. Here, we develop a framework for quantifying the influence author reputation has on a publication’s future impact. We find author reputation plays a key role in driving a paper’s citation count early in its citation life cycle, before a tipping point, after which reputation has much less influence relative to the paper’s citation count. In science, perceived quality, and decisions made based on those perceptions, is increasingly linked to citation counts. Shedding light on the complex mechanisms driving these quantitative measures facilitates not only better evaluation of scientific outputs but also a more transparent evaluation of the scientists producing them.

Reputation and impact in academic careers
Alexander Michael Petersen, Santo Fortunato, Raj K. Pan, KimmoKaski, Orion Penner, Armando Rungi, Massimo Riccaboni, H. Eugene Stanley, and Fabio Pammolli

PNAS

http://dx.doi.org/10.1073/pnas.1323111111

Multilayer stochastic block models reveal the multilayer structure of complex networks

In complex systems, the network of interactions we observe between system's components is the aggregate of the interactions that occur through different mechanisms or layers. Recent studies reveal that the existence of multiple interaction layers can have a dramatic impact in the dynamical processes occurring on these systems. However, these studies assume that the interactions between systems components in each one of the layers are known, while typically for real-world systems we do not have that information. Here, we address the issue of uncovering the different interaction layers from aggregate data by introducing multilayer stochastic block models (SBMs), a generalization of single-layer SBMs that considers different mechanisms of layer aggregation. First, we find the complete probabilistic solution to the problem of finding the optimal multilayer SBM for a given aggregate observed network. Because this solution is computationally intractable, we propose an approximation that enables us to verify that multilayer SBMs are more predictive of network structure in real-world complex systems.

Multilayer stochastic block models reveal the multilayer structure of complex networks
Toni Valles-Catala, Francesco A. Massucci, Roger Guimera, Marta Sales-Pardo

http://arxiv.org/abs/1411.1098

His brain, her brain?

There is a long history of scientific inquiry about what role biological sex plays in differences between brain function in human males and females. Greater knowledge of the influence of biological sex on the human brain promises much-needed insights into brain function and especially dysfunctions that differentially affect the sexes (1). Certainly, advancing technologies and an increasing wealth of data (with more sophisticated analyses) should prompt robust future research—carefully conducted and well replicated—that can elucidate sex effects in the brain. However, this field of research has spurred an equally long history of debate as to whether inherent differences in brains of males and females predispose the sexes to stereotypical behaviors, or whether such claims reinforce and legitimate traditional gender stereotypes and roles in ways that are not scientifically justified—so-called neurosexism. Although this topic remains controversial, a commonly held belief is that the psyches of females and males are highly distinct. These differences are perceived as natural, fixed, and invariant across time and place (2), presumably due to unique female versus male brain circuitry that is largely fixed by a sexually differentiated genetic blueprint. A major challenge in the field is to crtically view previous experimental findings, as well as design future studies, outside the framework of this dichotomous model. Here, gender scholarship can hasten scientific progress by revealing the implicit assumptions that can give rise to inadvertent neurosexism.

His brain, her brain?
Cordelia Fine

Science 21 November 2014: 
Vol. 346 no. 6212 pp. 915-916 
http://dx.doi.org/10.1126/science.1262061

Statistical physics of crime: A review

Containing the spreading of crime in urban societies remains a major challenge. Empirical evidence suggests that, left unchecked, crimes may be recurrent and proliferate. On the other hand, eradicating a culture of crime may be difficult, especially under extreme social circumstances that impair the creation of a shared sense of social responsibility. Although our understanding of the mechanisms that drive the emergence and diffusion of crime is still incomplete, recent research highlights applied mathematics and methods of statistical physics as valuable theoretical resources that may help us better understand criminal activity. We review different approaches aimed at modeling and improving our understanding of crime, focusing on the nucleation of crime hotspots using partial differential equations, self-exciting point process and agent-based modeling, adversarial evolutionary games, and the network science behind the formation of gangs and large-scale organized crime. We emphasize that statistical physics of crime can relevantly inform the design of successful crime prevention strategies, as well as improve the accuracy of expectations about how different policing interventions should impact malicious human activity deviating from social norms. We also outline possible directions for future research, related to the effects of social and coevolving networks and to the hierarchical growth of criminal structures due to self-organization.

Statistical physics of crime: A review
Maria R. D'Orsogna, MatjazPerc

http://arxiv.org/abs/1411.1743

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