Mapping the physics research space: a machine learning approach

Chinazzi, Matteo; Gonçalves, Bruno; Zhang, Qian; Vespignani, Alessandro

doi:10.1140/epjds/s13688-019-0210-z

Cited by 29 publications

(28 citation statements)

References 85 publications

(60 reference statements)

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“…In spite of this, some typical patterns of influence, such as cases of one field absorbing knowledge from another field, or two fields mutually influencing each other, clearly emerge at the microscopic scale. Our findings provide insights into the basic mechanisms of knowledge exchange in science, and can turn very useful to understand the dynamics of scientific production and the growth of novelties in scientific domains 32 – 34 .…”

Section: Introductionmentioning

confidence: 88%

The evolution of knowledge within and across fields in modern physics

Sun

Latora

2020

Sci Rep

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The exchange of knowledge across different areas and disciplines plays a key role in the process of knowledge creation, and can stimulate innovation and the emergence of new fields. We develop here a quantitative framework to extract significant dependencies among scientific disciplines and turn them into a time-varying network whose nodes are the different fields, while the weighted links represent the flow of knowledge from one field to another at a given period of time. Drawing on a comprehensive data set on scientific production in modern physics and on the patterns of citations between articles published in the various fields in the last 30 years, we are then able to map, over time, how the ideas developed in a given field in a certain time period have influenced later discoveries in the same field or in other fields. The analysis of knowledge flows internal to each field displays a remarkable variety of temporal behaviours, with some fields of physics showing to be more self-referential than others. The temporal networks of knowledge exchanges across fields reveal cases of one field continuously absorbing knowledge from another field in the entire observed period, pairs of fields mutually influencing each other, but also cases of evolution from absorbing to mutual or even to back-nurture behaviors.

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Section: Introductionmentioning

confidence: 88%

The evolution of knowledge within and across fields in modern physics

Sun

Latora

2020

Sci Rep

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“…Based on results shown in section S4, the physics knowledge space remained rather stable over the time span considered. A valuable alternative approach to take into account the temporal evolution of the physics knowledge space is provided by Chinazzi et al 20…”

Section: Model Extensions and Robustness Checksmentioning

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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“…In constrast, the work of Chinazzi et al . [ 7 ] is based on a machine learning method—the StarSpace algorithm [ 8 ]—to create embeddings (vector representations) for each reseach field, henceforth referred as Embedding model. Jaffe et al .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the state-of-the-art in mapping research spaces: A Brazilian case study

Azevedo

Murai

2021

PLoS ONE

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Scientific knowledge cannot be seen as a set of isolated fields, but as a highly connected network. Understanding how research areas are connected is of paramount importance for adequately allocating funding and human resources (e.g., assembling teams to tackle multidisciplinary problems). The relationship between disciplines can be drawn from data on the trajectory of individual scientists, as researchers often make contributions in a small set of interrelated areas. Two recent works propose methods for creating research maps from scientists’ publication records: by using a frequentist approach to create a transition probability matrix; and by learning embeddings (vector representations). Surprisingly, these models were evaluated on different datasets and have never been compared in the literature. In this work, we compare both models in a systematic way, using a large dataset of publication records from Brazilian researchers. We evaluate these models’ ability to predict whether a given entity (scientist, institution or region) will enter a new field w.r.t. the area under the ROC curve. Moreover, we analyze how sensitive each method is to the number of publications and the number of fields associated to one entity. Last, we conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.

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Mapping the physics research space: a machine learning approach

Cited by 29 publications

References 85 publications

The evolution of knowledge within and across fields in modern physics

The evolution of knowledge within and across fields in modern physics

Knowledge and social relatedness shape research portfolio diversification

Evaluating the state-of-the-art in mapping research spaces: A Brazilian case study

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