Connexion
S'inscrire
Réinitialiser le mot de passe
Publier & Distribuer
Solutions d'édition
Solutions de distribution
Thèmes
Architecture et design
Arts
Business et économie
Chimie
Chimie industrielle
Droit
Géosciences
Histoire
Informatique
Ingénierie
Intérêt général
Linguistique et sémiotique
Littérature
Mathématiques
Musique
Médecine
Pharmacie
Philosophie
Physique
Sciences bibliothécaires et de l'information, études du livre
Sciences des matériaux
Sciences du vivant
Sciences sociales
Sport et loisirs
Théologie et religion
Études classiques et du Proche-Orient ancient
Études culturelles
Études juives
Publications
Journaux
Livres
Comptes-rendus
Éditeurs
Blog
Contact
Chercher
EUR
USD
GBP
Français
English
Deutsch
Polski
Español
Français
Italiano
Panier
Home
Journaux
Journal of Data and Information Science
Édition 4 (2019): Edition 2 (May 2019)
Accès libre
Node2vec Representation for Clustering Journals and as A Possible Measure of Diversity
Zhesi Shen
Zhesi Shen
,
Fuyou Chen
Fuyou Chen
,
Liying Yang
Liying Yang
et
Jinshan Wu
Jinshan Wu
| 07 juin 2019
Journal of Data and Information Science
Édition 4 (2019): Edition 2 (May 2019)
À propos de cet article
Article précédent
Article suivant
Résumé
Article
Figures et tableaux
Références
Auteurs
Articles dans cette édition
Aperçu
PDF
Citez
Partagez
Article Category:
Research Paper
Publié en ligne:
07 juin 2019
Pages:
79 - 92
Reçu:
03 avr. 2019
Accepté:
09 mai 2019
DOI:
https://doi.org/10.2478/jdis-2019-0010
Mots clés
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
Aperçu