Increasing trend of scientists to switch between topics

Zeng, An; Shen, Zhesi; Zhou, Jingming; Fan, Ying; Di, Zengru; Wang, Yougui; Stanley, H. Eugene; Havlin, Shlomo

doi:10.1038/s41467-019-11401-8

Cited by 113 publications

(88 citation statements)

References 43 publications

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“…To directly compute the efficiency distribution with the discrete joint probability distribution p(R, A) we follow the method described in the "Methods" section [Eqs. (18) and (19)]. The resulting efficiency distribution is asymptotically exact in the sense that, since the support for the distributions of A and R is N , an infinite number of terms would be required to actually obtain exact results, but larger values of A and R have increasingly smaller probabilities, carrying progressively lower weight on the computation and enabling the results to converge for a finite number of terms.…”

Section: Description Of the Modelsmentioning

confidence: 99%

“…Since A and R are considered independent, their discrete joint probability distribution is p(R, A) = p(R)p(A) . The PDF of efficiency can be obtained by plugging this expression in (18) and (19) of "Methods". However, for this model we have left out the results of the discrete methodology because we have derived an exact analytical expression.…”

Section: Independent Variables Model In the Inv Model A And R Are Comentioning

confidence: 99%

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Impact of individual actions on the collective response of social systems

Martin-Gutierrez

Losada

Benito

2020

Sci Rep

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in a social system individual actions have the potential to trigger spontaneous collective reactions. the way and extent to which the activity (number of actions-A) of an individual causes or is connected to the response (number of reactions-R) of the system is still an open question. We measure the relationship between activity and response with the distribution of efficiency, a metric defined as η = R/A. Generalizing previous results, we show that the efficiency distribution presents a universal structure in three systems of different nature: Twitter, Wikipedia and the scientific citations network. To understand this phenomenon, we develop a theoretical framework composed of three minimal statistical models that contemplate different levels of dependence between A and R. the models not only are able to reproduce the empirical activity-response data but also can serve as baselines or null models for more elaborated and domain-specific approaches. Due to humans' social nature, the actions of individuals hold the potential to trigger spontaneous collective reactions, leading to complex dynamics. In order to understand human collective behavior, it is necessary to find the laws that relate the individual actions to the collective response of social systems. This topic has received considerable attention and has been approached from several perspectives 1-4. From diffusion on networked systems, a field which studies the spread of diseases or information and the emergence of cascading phenomena 5-7 to virality, a property of certain pieces of information that generate a wide response in social systems 8-10. Other works focus on the Influence Maximization problem, taking advantage of the diffusion mechanisms to find a set of individuals that maximize the response 11-13. Alternatively, the field of control theory aims to steer the collective behavior of a system by controlling the activity of a few individuals 14, 15. Our goal in this work is to develop a theoretical framework that relates the number of actions performed by an actor (an agent or individual) embedded in a social system; that is, her activity (A), and the number of reactions that these actions trigger in her peers, or response (R). To relate these two magnitudes we generalize the efficiency metric (η = R A), introduced by Morales et al. in the context of Twitter 16 , to other social systems. We follow a well established modeling approach in social physics: explain the macroscopic properties of the system assuming the simplest microscopic interactions between the actors to extract the most fundamental laws 17-23. The macroscopic property in which we focus is the distribution of efficiency. We have used this metric to analyze three kinds of social systems of different nature: social networks, collaborative networks and citations networks. In particular, we have worked with 14 Twitter conversations around different issues in Spain, Turkey, Palestine, Argentina and Colombia, the editions of the English Wikipedia and the scientific citations data of authors f...

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Section: Description Of the Modelsmentioning

confidence: 99%

Section: Independent Variables Model In the Inv Model A And R Are Comentioning

confidence: 99%

Impact of individual actions on the collective response of social systems

Martin-Gutierrez

Losada

Benito

2020

Sci Rep

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“…In particular, this model simulates how random geometric graphs grow in the hyperbolic space, generating realistic networks with clustering, small-worldness, scale-freeness, and rich-clubness. In this part, we generate the nPSO hyperbolic networks with community with these parameters: N � [100, 500, 1000] (network size), 〈k〉 0.5 � [4,8,10] (half of average degree), T � [0.1, 0.3, 0.5, 0.7] (temperature, inversely related to the clustering coefficient), n c � [3,6,9] (number of communities), and c nPSO � [2,3] (power-law degree distribution exponent). We also compare the SAS algorithm with state-ofthe-art community detection algorithms.…”

Section: E Npso Benchmarkmentioning

confidence: 99%

“…Among others, one salient property commonly observed in many complex networks is the community structure, i.e., the organization of nodes in di erent groups, with many edges connecting nodes of the same group and comparatively fewer connections among nodes of di erent groups [4][5][6][7]. For instance, in a scienti c citation network, communities are sets of scienti c papers on the same topic or in a similar research eld [8], while in protein-protein interaction networks, proteins working in the same biological process (or being in the same cellular component) interact with each other. Moreover, the community structure has been shown to have strong impacts on epidemic dynamics [9,10] and link prediction.…”

Section: Introductionmentioning

confidence: 99%

Community Detection with Self‐Adapting Switching Based on Affinity

2019

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Community structures in complex networks play an important role in researching network function. Although there are various algorithms based on affinity or similarity, their drawbacks are obvious. They perform well in strong communities, but perform poor in weak communities. Experiments show that sometimes, community detection algorithms based on a single affinity do not work well, especially for weak communities. So we design a self-adapting switching (SAS) algorithm, where weak communities are detected by combination of two affinities. Compared with some state-of-the-art algorithms, the algorithm has a competitive accuracy and its time complexity is near linear. Our algorithm also provides a new framework of combination algorithm for community detection. Some extensive computational simulations on both artificial and real-world networks confirm the potential capability of our algorithm.

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“…In order to reconstruct the macroscopic patterns that drive such evolution, the authors proposed a random walk model over a stylized knowledge space, which reproduces empirical observations thanks to the inclusion of key features such as heterogeneity, subject proximity and recency. Finally, Zeng et al 10 analysed the dynamics of "topic switching" by exploring co-citation networks. Results suggest a growing propensity to switch among topics but also that such a strategy might hamper productivity, especially for early-career researchers.…”

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confidence: 99%

Knowledge and social relatedness shape research portfolio diversification

Tripodi

Chiaromonte

Lillo

2020

Sci Rep

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Scientific discovery is shaped by scientists' choices and thus by their career patterns. The increasing knowledge required to work at the frontier of science makes it harder for an individual to embark on unexplored paths. Yet collaborations can reduce learning costs-albeit at the expense of increased coordination costs. In this article, we use data on the publication histories of a very large sample of physicists to measure the effects of knowledge and social relatedness on their diversification strategies. Using bipartite networks, we compute a measure of topic similarity and a measure of social proximity. We find that scientists' strategies are not random, and that they are significantly affected by both. Knowledge relatedness across topics explains ≈ 10% of logistic regression deviances and social relatedness as much as ≈ 30% , suggesting that science is an eminently social enterprise: when scientists move out of their core specialization, they do so through collaborations. Interestingly, we also find a significant negative interaction between knowledge and social relatedness, suggesting that the farther scientists move from their specialization, the more they rely on collaborations. Our results provide a starting point for broader quantitative analyses of scientific diversification strategies, which could also be extended to the domain of technological innovation-offering insights from a comparative and policy perspective.

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Increasing trend of scientists to switch between topics

Cited by 113 publications

References 43 publications

Impact of individual actions on the collective response of social systems

Impact of individual actions on the collective response of social systems

Community Detection with Self‐Adapting Switching Based on Affinity

Knowledge and social relatedness shape research portfolio diversification

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