About: Identifying key papers within a journal via network centrality measures

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Identifying key papers within a journal via network centrality measures Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Identifying key papers within a journal via network centrality measures
Creator	Christopher, • Diallo, Saikou Gore, Ross Lynch, J Padilla, Jose
Source	Medline; PMC
abstract	This article examines the extent to which existing network centrality measures can be used (1) as filters to identify a set of papers to start reading within a journal and (2) as article-level metrics to identify the relative importance of a paper within a journal. We represent a dataset of published papers in the Public Library of Science (PLOS) via a co-citation network and compute three established centrality metrics for each paper in the network: closeness, betweenness, and eigenvector. Our results show that the network of papers in a journal is scale-free and that eigenvector centrality (1) is an effective filter and article-level metric and (2) correlates well with citation counts within a given journal. However, closeness centrality is a poor filter because articles fit within a small range of citations. We also show that betweenness centrality is a poor filter for journals with a narrow focus and a good filter for multidisciplinary journals where communities of papers can be identified.
has issue date	2016-02-15(xsd:dateTime)
bibo:doi	10.1007/s11192-016-1891-8
bibo:pmid	32214550
has license	no-cc
sha1sum (hex)	23c3a53f431fa237d8ecc3d16391f083582eacd5
schema:url	https://doi.org/10.1007/s11192-016-1891-8
resource representing a document's title	Identifying key papers within a journal via network centrality measures
has PubMed Central identifier	PMC7088853
has PubMed identifier	32214550
schema:publication	Scientometrics
resource representing a document's body	covid:23c3a53f431fa237d8ecc3d16391f083582eacd5#body_text
is schema:about of	named entity 'ARTICLE-LEVEL METRICS' named entity 'USED' named entity 'MEASURES' named entity 'EXTENT' named entity 'READING' named entity 'RESULTS' named entity 'JOURNAL' named entity 'FILTER' named entity 'JOURNAL ' named entity 'RANGE' named entity 'LIBRARY' named entity 'IS A' named entity 'WHERE' named entity 'FILTERS' named entity 'GIVEN' named entity 'CITATION' named entity 'ESTABLISHED' named entity 'compute' named entity 'effective' named entity 'metrics' named entity 'measures' named entity 'PAPERS' covid:arg/23c3a53f431fa237d8ecc3d16391f083582eacd5 named entity 'GOOD' named entity 'MULTIDISCIPLINARY' named entity 'START' named entity 'IDENTIFYING' named entity 'JOURNAL ' named entity 'FREE' named entity 'CITATIONS' named entity 'DATASET' named entity 'EFFECTIVE' named entity 'COMMUNITIES' named entity 'COUNTS' named entity 'POOR' named entity 'SCIENCE' named entity 'REPRESENT' named entity '282' named entity 'PAPERS' named entity 'MEASURES' named entity 'NETWORK' named entity 'CENTRALITY' named entity 'FIT' named entity 'PUBLIC' named entity 'IDENTIFIED' named entity 'LEVEL' named entity 'SET' named entity 'NARROW' named entity 'OUR' named entity 'PUBLISHED' named entity 'KEY' named entity 'RELATIVE' named entity 'NETWORK' named entity 'SCALE' named entity 'ARTICLE' named entity 'CENTRALITY' named entity 'FOCUS' named entity 'METRIC' named entity 'METRICS' named entity 'SMALL' named entity 'PAPER' named entity 'EIGENVECTOR' named entity 'TO IDENTIFY' named entity 'reading' named entity 'multidisciplinary' named entity 'Public Library' named entity 'measures' named entity 'identify' named entity 'PLOS' named entity 'citations' named entity 'key' named entity 'closeness centrality' named entity 'article-level metric' named entity 'citation counts' named entity 'number of citations' named entity 'PLOS Biology' named entity 'eigenvector centrality'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software