Small world of the miRNA science drives its publication dynamics

Many scientific articles became available in the digital form which allows for querying articles data, and specifically the automated metadata gathering, which includes the affiliation data. This in turn can be used in the quantitative characterization of the scientific field, such as organizations identification, and analysis of the co-authorship graph of those organizations to extract the underlying structure of science. In our work, we focus on the miRNA science field, building the organization co-authorship network to provide the higher-level analysis of scientific community evolution rather than analyzing author-level characteristics. To tackle the problem of the institution name writing variability, we proposed the k-mer/n-gram boolean feature vector sorting algorithm, KOFER in short. This approach utilizes the fact that the contents of the affiliation are rather consistent for the same organization, and to account for writing errors and other organization name variations within the affiliation metadata field, it converts the organization mention within the affiliation to the K-Mer (n-gram) Boolean presence vector. Those vectors for all affiliations in the dataset are further lexicographically sorted, forming groups of organization mentions. With that approach, we clustered the miRNA field affiliation dataset and extracted unique organization names, which allowed us to build the co-authorship graph on the organization level. Using this graph, we show that the growth of the miRNA field is governed by the small-world architecture of the scientific institution network and experiences power-law growth with exponent 2.64 ± 0.23 for organization number, in accordance with network diameter, proposing the growth model for emerging scientific fields. The first miRNA publication rate of an organization interacting with already publishing organization is estimated as 0.184 ± 0.002 year–1. 

1. Goffman W., Newill V.A. Generalization of epidemic theory. An application to the transmission of ideas. Nature. 1964;204(4955):225228. DOI 10.1038/204225a0.

2. Humphries M.D., Gurney K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One. 2008;3(4):e0002051. DOI 10.1371/journal.pone.0002051.

3. Leydesdorff L., Wagner C., Park H., Adams J. International collaboration in science: the global map and the network. Prof. Inf. 2013; 22(1):1-18. DOI 10.3145/epi.2013.ene.12.

4. Liu M., Li D., Qin P., Liu C., Wang H., Wang F. Epidemics in interconnected small-world networks. PLoS One. 2015;10(3):e0120701. DOI 10.1371/journal.pone.0120701.

5. Muldoon S., Bridgeford E., Bassett D. Small-world propensity and weighted brain networks. Sci. Rep. 2016;6:22057. DOI 10.1038/srep22057.

6. Newman M.E.J., Moore C., Watts D.J. Mean-field solution of the smallworld network model. Phys. Rev. Lett. 2000;84(14):3201-3204. DOI 10.1103/PhysRevLett.84.3201.

7. Ribeiro L., Rapini M., Silva L., Albuquerque E.M. Growth patterns of the network of international collaboration in science. Scientometrics. 2018;114:159-179. DOI 10.1007/s11192-017-2573-x.

8. Shi Y., Guan J. Small-world network effects on innovation: evidences from nanotechnology patenting. J. Nanopart. Res. 2016;18:329. DOI 10.1007/s11051-016-3637-1.

9. Vazquez A. Spreading dynamics on small-world networks with connectivity fluctuations and correlations. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys. 2006;74:056101. DOI 10.1103/PhysRevE.74.056101.

10. Wagner C., Leydesdorff L. Network structure, self-organization and the growth of international collaboration in science. Res. Policy. 2005; 34(10):1608-1618. DOI 10.1016/j.respol.2005.08.002.

11. Watts D.J., Strogatz S.H. Collective dynamics of ‘small-world’ networks. Nature. 1998;393(6684):440-442. DOI 10.1038/30918.