<div class="csl-bib-body">
<div class="csl-entry">Taheri, S. M., Mahyar, H., Firouzi, M., Ghalebi K., E., Grosu, R., & Movaghar, A. (2017). HellRank: a Hellinger-based centrality measure for bipartite social networks. <i>Social Network Analysis and Mining</i>. https://doi.org/10.1007/s13278-017-0440-7</div>
</div>
Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node uses local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.
en
dc.language
English
-
dc.language.iso
en
-
dc.publisher
Springer Vienna
-
dc.relation.ispartof
Social Network Analysis and Mining
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
Bipartite social networks
en
dc.subject
Top-k central nodes
en
dc.subject
Hellinger distance
en
dc.subject
Recommender systems
en
dc.title
HellRank: a Hellinger-based centrality measure for bipartite social networks
en
dc.type
Article
en
dc.type
Artikel
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.rights.holder
The Author(s) 2017
-
dc.type.category
Original Research Article
-
tuw.peerreviewed
false
-
tuw.version
vor
-
dcterms.isPartOf.title
Social Network Analysis and Mining
-
tuw.publication.orgunit
E191 - Institut für Computer Engineering
-
tuw.publisher.doi
10.1007/s13278-017-0440-7
-
dc.identifier.libraryid
AC15188640
-
dc.identifier.urn
urn:nbn:at:at-ubtuw:3-4235
-
dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
item.languageiso639-1
en
-
item.openairetype
research article
-
item.grantfulltext
open
-
item.fulltext
with Fulltext
-
item.cerifentitytype
Publications
-
item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
-
item.openaccessfulltext
Open Access
-
crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems
-
crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems
-
crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems