Login
Registrieren
Passwort zurücksetzen
Veröffentlichen & Verteilen
Verlagslösungen
Vertriebslösungen
Themen
Allgemein
Altertumswissenschaften
Architektur und Design
Bibliotheks- und Informationswissenschaft, Buchwissenschaft
Biologie
Chemie
Geowissenschaften
Geschichte
Industrielle Chemie
Informatik
Jüdische Studien
Kulturwissenschaften
Kunst
Linguistik und Semiotik
Literaturwissenschaft
Materialwissenschaft
Mathematik
Medizin
Musik
Pharmazie
Philosophie
Physik
Rechtswissenschaften
Sozialwissenschaften
Sport und Freizeit
Technik
Theologie und Religion
Wirtschaftswissenschaften
Veröffentlichungen
Zeitschriften
Bücher
Konferenzberichte
Verlage
Blog
Kontakt
Suche
EUR
USD
GBP
Deutsch
English
Deutsch
Polski
Español
Français
Italiano
Warenkorb
Home
Zeitschriften
Journal of Data and Information Science
Band 4 (2019): Heft 2 (May 2019)
Uneingeschränkter Zugang
Node2vec Representation for Clustering Journals and as A Possible Measure of Diversity
Zhesi Shen
Zhesi Shen
,
Fuyou Chen
Fuyou Chen
,
Liying Yang
Liying Yang
und
Jinshan Wu
Jinshan Wu
| 07. Juni 2019
Journal of Data and Information Science
Band 4 (2019): Heft 2 (May 2019)
Über diesen Artikel
Vorheriger Artikel
Nächster Artikel
Zusammenfassung
Artikel
Figuren und Tabellen
Referenzen
Autoren
Artikel in dieser Ausgabe
Vorschau
PDF
Zitieren
Teilen
Article Category:
Research Paper
Online veröffentlicht:
07. Juni 2019
Seitenbereich:
79 - 92
Eingereicht:
03. Apr. 2019
Akzeptiert:
09. Mai 2019
DOI:
https://doi.org/10.2478/jdis-2019-0010
Schlüsselwörter
Science mapping
,
Diversity
,
Graph embedding
,
Vector norm
© 2019 Zhesi Shen, Fuyou Chen, Liying Yang, Jinshan Wu, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Figure 1
Map of scientific journals. Colors of dots mean the corresponding ESI categories of journals. Dots in red with black border are journals indexed as multidisciplinary, of which we list only a few on the map.
Figure 2
(a) Our vector-based clustering of journals compared with several existing journal classification systems JCR, VOS, ESI and LCAS. (b) We also compare resulted clusters using various dimensions of the node2vec vectors and we find there is not much differences among d = 32, d = 64 and d = 128: the similarity of 64–32 and 128–32 are much higher than that of 8–32 while 16–32 is somewhere in between. Also when Vec clusters with d = 8, 16, 32, 64, 128 are compared against VOS, we find as long as d > 16, increasing d does not make a big difference. Considering both performance and computational cost, we only report the results of d = 32.
Figure 3
Scatter plot of vector norms versus node centrality. Node centrality is measured as the node occurrence frequency in the random walk series generated for Node2Vec. Orange dots represent journals indexed in Multidisciplinary Science.
Figure 4
Diversity of journals calculated using similarity measured by (a) vector vn learned from node2vec and (b) vector vc. Journals indexed as Multidisciplinary are colored according to their JIFs with blue implying low JIF and red implying high JIF as shown in the right legend. The citing diversity of journal i is measured based on its referenced journals, and cited diversity is measured based on the journals citing it.
Figure 5
A graphic summary of our work: concepts and connections in red are the ones that have been implemented in the current work while the ones in green can be topics of future investigation. The rest of concepts and connections have been proposed and implemented in earlier studies, see for example, (Mikolov et al., 2013) and (Grover & Leskovec, 2016).
The “King - Man + Woman = Queen” test on the node2vec trained vectors of journals. Top-5 “Queen”-like journals are presented.
Example
Test 1
Test 2
Test 3
King
PLoS Comput. Biol
Man
Nat. Cell Biol
Woman
Phys. Rev. Lett
Genome Biol
J. Neurosci
Queen
J. Stat. Mech. Theory Exp
Bioinformatics
NeuroImage
Phys. Rev. E
BMC Bioinformatics
Biol. Cybern
Fluctuation Noise Lett
J. Comput. Biol
Front. Comput. Neurosci
EPL
BioData Min
Cereb. Cortex
Eur. Phys. J. B
J. Bioinform. Comput. Biol
J. Comput. Neurosci
Vorschau