Predicting triadic closure in networks using communicability distance functions

Ernesto Estrada, Francesca Arrigo

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
161 Downloads (Pure)


We propose a communication-driven mechanism for predicting triadic closure in complex networks. It is mathematically formulated on the basis of communicability distance functions that account for the quality of communication between nodes in the network. We study 25 real-world networks and show that the proposed method correctly predicts 20% of triadic closures in these networks, in contrast to the 7.6% predicted by a random mechanism. We also show that the communication-driven method outperforms the random mechanism in explaining the clustering coefficient, average path length, and average communicability. The new method also displays some interesting features with regards to optimizing communication in networks.
Original languageEnglish
Pages (from-to)1725-1744
Number of pages20
JournalSIAM Journal on Applied Mathematics
Issue number4
Early online date6 Aug 2015
Publication statusPublished - 2015


  • network analysis
  • triangles
  • triadic closure
  • communicability distances
  • adjacency matrix
  • matrix functions


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