Uncovering the nucleus of social networks

Braulio Dumba, Zhi Li Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Many social network studies have focused on identifying communities through clustering or partitioning a large social network into smaller parts. While community structure is important in social network analysis, relatively little attention has been paid to the problem of “core structure” analysis in many social networks. Intuitively, one may expect that many social networks possess some sort of a “core” which holds various parts of the network (or constituent “communities”) together. We believe that it is just as important to uncover and extract the “core” structure - referred to as the “nucleus” in this paper - of a social network as to identify its community structure. In this paper, we propose a scalable and effective procedure to uncover the “nucleus” of social networks by building upon and generalizing ideas from the existing k-shell decomposition approach. We employ our approach to uncover the nucleus in several example communication, collaboration, interaction, location-based and online social networks. Our methodology is very scalable and can also be applied to massive networks (hundreds million nodes and billion edges).

Original languageEnglish (US)
Title of host publicationWebSci 2018 - Proceedings of the 10th ACM Conference on Web Science
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450355636
StatePublished - May 15 2018
Event10th ACM Conference on Web Science, WebSci 2018 - Amsterdam, Netherlands
Duration: May 27 2018May 30 2018

Publication series

NameWebSci 2018 - Proceedings of the 10th ACM Conference on Web Science


Other10th ACM Conference on Web Science, WebSci 2018

Bibliographical note

Funding Information:
This research was supported in part by DoD ARO MURI Award W911NF-12-1-0385, DTRA grant HDTRA1- 14-1-0040, NSF grant CNS-1411636, CNS-1618339 and CNS-1617729.

Publisher Copyright:
© 2018 Association for Computing Machinery.


  • K-shell decomposition
  • Network core
  • Social network


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