Weighted node degree centrality for hypergraphs

Komal Kapoor, Dhruv Sharma, Jaideep Srivastava

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

16 Scopus citations

Abstract

Many real-world social interactions involve multiple people, for e.g., authors collaborating on a paper, email exchanges made in a company and task-oriented teams in workforce. Simple graph representation of these activities destroys the group structure present in them. Hypergraphs have recently emerged as a better tool for modeling group interactions. However, methods in social hypernetwork analysis haven't kept pace. In this work, we extend the concept of node degree centrality to hypergraphs. We validate our proposed measures using alternate measures of influence available to us using two datasets namely, the DBLP dataset of scientific collaborations and the group network in a popular Chinese multi-player online game called CR3. We discuss several schemes for assigning weights to hyperedges and compare them empirically. Finally, we define separate weak and strong tie node degree centralities which improves performance of our models. Weak tie degree centrality is found to be a better predictor of influence in hypergraphs than strong tie degree centrality.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 IEEE 2nd International Network Science Workshop, NSW 2013
Pages152-155
Number of pages4
DOIs
StatePublished - Oct 28 2013
Event2013 IEEE 2nd International Network Science Workshop, NSW 2013 - West Point, NY, United States
Duration: Apr 29 2013May 1 2013

Publication series

NameProceedings of the 2013 IEEE 2nd International Network Science Workshop, NSW 2013

Other

Other2013 IEEE 2nd International Network Science Workshop, NSW 2013
CountryUnited States
CityWest Point, NY
Period4/29/135/1/13

Keywords

  • Hypergraph
  • centrality
  • degree

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