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

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Stochastic block model reveals maps of citation patterns and their evolution in time. / Hric, Darko; Kaski, Kimmo; Kivelä, Mikko.

In: Journal of Informetrics, Vol. 12, No. 3, 01.08.2018, p. 757-783.

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@article{fe1ead2f657f4feda43ab7157e1e3dbf,
title = "Stochastic block model reveals maps of citation patterns and their evolution in time",
abstract = "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.",
keywords = "Citation networks, Evolution of science, Stochastic block model, Web of science",
author = "Darko Hric and Kimmo Kaski and Mikko Kivel{\"a}",
note = "| openaire: EC/H2020/654024/EU//SoBigData",
year = "2018",
month = "8",
day = "1",
doi = "10.1016/j.joi.2018.05.004",
language = "English",
volume = "12",
pages = "757--783",
journal = "Journal of Informetrics",
issn = "1751-1577",
publisher = "Elsevier BV",
number = "3",

}

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TY - JOUR

T1 - Stochastic block model reveals maps of citation patterns and their evolution in time

AU - Hric, Darko

AU - Kaski, Kimmo

AU - Kivelä, Mikko

N1 - | openaire: EC/H2020/654024/EU//SoBigData

PY - 2018/8/1

Y1 - 2018/8/1

N2 - 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.

AB - 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.

KW - Citation networks

KW - Evolution of science

KW - Stochastic block model

KW - Web of science

UR - http://www.scopus.com/inward/record.url?scp=85049518817&partnerID=8YFLogxK

U2 - 10.1016/j.joi.2018.05.004

DO - 10.1016/j.joi.2018.05.004

M3 - Article

VL - 12

SP - 757

EP - 783

JO - Journal of Informetrics

JF - Journal of Informetrics

SN - 1751-1577

IS - 3

ER -

ID: 26606004