Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Inferring Offline Hierarchical Ties from Online Social Networks
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: Proceedings of the companion publication of the 23rd international conference on World wide web companion, Association for Computing Machinery (ACM), 2014, 1261-1266 p.Conference paper, Published paper (Refereed)
Abstract [en]

Social networks can represent many different types of relationships between actors, some explicit and some implicit. For example, email communications between users may be represented explicitly in a network, while managerial relationships may not. In this paper we focus on analyzing explicit interactions among actors in order to detect hierarchical social relationships that may be implicit. We start by employing three well-known ranking-based methods, PageRank, Degree Centrality, and Rooted-PageRank (RPR) to infer such implicit relationships from interactions between actors. Then we propose two novel approaches which take into account the time-dimension of interactions in the process of detecting hierarchical ties. We experiment on two datasets, the Enron email dataset to infer manager-subordinate relationships from email exchanges, and a scientific publication co-authorship dataset to detect PhD advisor-advisee relationships from paper co-authorships. Our experiments show that time-based methods perform considerably better than ranking-based methods. In the Enron dataset, they detect 48% of manager-subordinate ties versus 32% found by Rooted-PageRank. Similarly, in co-author dataset, they detect 62% of advisor-advisee ties compared to only 39% by Rooted-PageRank.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2014. 1261-1266 p.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-108636DOI: 10.1145/2567948.2580070ISBN: 978-1-4503-2745-9 (print)OAI: oai:DiVA.org:su-108636DiVA: diva2:759829
Conference
International World Wide Web Conference, Seoul, Republic of Korea, April 7 -11, 2014
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2014-12-11Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full texthttp://dl.acm.org/citation.cfm?id=2580070&CFID=607268695&CFTOKEN=10611098

Search in DiVA

By author/editor
Papapetrou, Panagiotis
By organisation
Department of Computer and Systems Sciences
Information Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 24 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf