Quantifying the diaspora of knowledge in the last century

Domenico, Manlio De; Omodei, Elisa; Arenas, Àlex

doi:10.1007/s41109-016-0017-9

Cited by 19 publications

(23 citation statements)

References 28 publications

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“…In this bucket brigade, A (chain) ij = 1 for j = i ± 1 and A (chain) ij = 0 otherwise. Although one can choose inter-layer coupling matrices other than A (chain) for the inter-layer couplings [24,72] (and much of our approach can be generalized to other choices of coupling), we restrict our attention to nearest-neighbor coupling of layers.…”

Section: )mentioning

confidence: 99%

Eigenvector-Based Centrality Measures for Temporal Networks

Taylor¹,

Myers²,

Clauset³

et al. 2017

Multiscale Model. Simul.

160

198

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Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes’ centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court.

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Section: )mentioning

confidence: 99%

Eigenvector-Based Centrality Measures for Temporal Networks

Taylor¹,

Myers²,

Clauset³

et al. 2017

Multiscale Model. Simul.

160

198

View full text Add to dashboard Cite

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“…Uncovering the mechanisms governing research activities of individual scientists and their evolution with time is critical for understanding and managing a wide range of issues in science, from training of scientists to collective discovery of new knowledge 1–5 . The increased availability of large data sets that capture research activities creates an unprecedented opportunity to explore the dynamical patterns of scientific production and reward using state-of-the-art mathematical and computational tools 6–8 .…”

Section: Introductionmentioning

confidence: 99%

Increasing trend of scientists to switch between topics

Zeng¹,

Shen²,

Zhou³

et al. 2019

Nat Commun

100

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Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features.

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“…The predecessor of MA, Microsoft Academic Search, was decommissioned towards the end of 2016 and attracted little bibliometric research. Harzing (2016) identified only six journal articles related to Microsoft Academic Search and bibliometrics. In contrast, MA has already spurred great interest in a short period of time and triggered several studies that focus on bibliometric topics, such as four studies on visualization and mapping (De Domenico, Omodei, & Arenas, 2016;Portenoy, Hullman, & West, 2016;Portenoy, & West, 2017, Tan et al, 2016. Furthermore, there are eleven studies that deal with the development of indicators and algorithms (Effendy & Yap, 2016;Effendy & Yap, 2017;Herrmannova & Knoth, 2016b;Luo, Gong, Hu, Duan, & Ma, 2016;Medo & Cimini, 2016;Ribas, Ueda, Santos, Ribeiro-Neto, & Ziviani, 2016;Sandulescu & Chiru, 2016;Wesley-Smith, Bergstrom, & West, 2016;Vaccario, Medo, Wider, & Mariani, 2017;Wilson, Mohan, Arif, Chaudhury, & Lall, 2016;Xiao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

The coverage of Microsoft Academic: analyzing the publication output of a university

Hug

Brändle

2017

Scientometrics

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This is the first detailed study on the coverage of Microsoft Academic (MA). Based on the complete and verified publication list of a university, the coverage of MA was assessed and compared with two benchmark databases, Scopus and Web of Science (WoS), on the level of individual publications. Citation counts were analyzed, and issues related to data retrieval and data quality were examined. A Perl script was written to retrieve metadata from MA based on publication titles. The script is freely available on GitHub. We find that MA covers journal articles, working papers, and conference items to a substantial extent and indexes more document types than the benchmark databases (e.g., working papers, dissertations). MA clearly surpasses Scopus and WoS in covering book-related document types and conference items but falls slightly behind Scopus in journal articles. The coverage of MA is favorable for evaluative bibliometrics in most research fields, including economics/business, computer/information sciences, and mathematics. However, MA shows biases similar to Scopus and WoS with regard to the coverage of the humanities, non-English publications, and open-access publications. Rank correlations of citation counts are high between MA and the benchmark databases. We find that the publication year is correct for 89.5% of all publications and the number of authors is correct for 95.1% of the journal articles. Given the fast and ongoing development of MA, we conclude that MA is on the verge of becoming a bibliometric superpower. However, comprehensive studies on the quality of MA metadata are still lacking.

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Quantifying the diaspora of knowledge in the last century

Cited by 19 publications

References 28 publications

Eigenvector-Based Centrality Measures for Temporal Networks

Eigenvector-Based Centrality Measures for Temporal Networks

Increasing trend of scientists to switch between topics

The coverage of Microsoft Academic: analyzing the publication output of a university

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