Stochastic block model reveals maps of citation patterns and their evolution in time

Darko Hric, Kimmo Kaski, Mikko Kivelä*

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

10 Sitaatiot (Scopus)
220 Lataukset (Pure)

Abstrakti

In this study we map out the large-scale structure of citation networks of science journals and follow their evolution in time by using stochastic block models (SBMs). The SBM fitting procedures are principled methods that can be used to find hierarchical grouping of journals that show similar incoming and outgoing citations patterns. These methods work directly on the citation network without the need to construct auxiliary networks based on similarity of nodes. We fit the SBMs to the networks of journals we have constructed from the data set of around 630 million citations and find a variety of different types of groups, such as communities, bridges, sources, and sinks. In addition we use a recent generalization of SBMs to determine how much a manually curated classification of journals into subfields of science is related to the group structure of the journal network and how this relationship changes in time. The SBM method tries to find a network of blocks that is the best high-level representation of the network of journals, and we illustrate how these block networks (at various levels of resolution) can be used as maps of science.

AlkuperäiskieliEnglanti
Sivut757-783
Sivumäärä27
JulkaisuJournal of Informetrics
Vuosikerta12
Numero3
DOI - pysyväislinkit
TilaJulkaistu - 1 elok. 2018
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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