Header menu link for other important links
X
Measuring network centrality using hypergraphs
Published in Association for Computing Machinery
2015
Volume: 18-21-March-2015
   
Pages: 59 - 68
Abstract
Networks abstracted as graph lose some information related to the super-dyadic relation among the nodes. We find natural occurrence of hyperedges in co-authorship, co-citation, social networks, e-mail networks, weblog networks etc. Treating these networks as hypergraph preserves the super-dyadic relations. But the primal graph or Gaifmann graph associated with hypergraphs converts each hyperedge to a clique losing again the n-ary relationship among nodes. We aim to measure Shapley Value based centrality on these networks without losing the super-dyadic information. For this purpose, we use co-operative games on single graph representation of a hypergraph such that Shapley value can be computed efficiently[1]. We propose several methods to generate simpler graphs from hypergraphs and study the efficacy of the centrality scores computed on these constructions. Copyright 2015 ACM.
About the journal
JournalData powered by TypesetACM International Conference Proceeding Series
PublisherData powered by TypesetAssociation for Computing Machinery
Open AccessYes
Concepts (11)
  •  related image
    Electronic mail
  •  related image
    Game theory
  •  related image
    CENTRALITY
  •  related image
    CO-AUTHORSHIPS
  •  related image
    DYADIC RELATIONS
  •  related image
    E-MAIL NETWORKS
  •  related image
    Hypergraph
  •  related image
    NETWORK CENTRALITIES
  •  related image
    Shapley value
  •  related image
    SINGLE GRAPH
  •  related image
    Graph theory