Exact and sampling methods for mining higher-order motifs in large hypergraphs

Quintino Francesco Lotito*, Federico Musciotto, Federico Battiston, Alberto Montresor

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

Network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the problem of counting occurrences of small sub-hypergraph patterns in very large hypergraphs, where higher-order interactions connect arbitrary numbers of system units. We show how directly exploiting higher-order structures speeds up the counting process compared to traditional data mining techniques for exact motif discovery. Moreover, with hyperedge sampling, performance is further improved at the cost of small errors in the estimation of motif frequency. We evaluate our method on several real-world datasets describing face-to-face interactions, co-authorship and human communication. We show that our approximated algorithm allows us to extract higher-order motifs faster and on a larger scale, beyond the computational limits of an exact approach.

Original languageEnglish
Pages (from-to)475-494
Number of pages20
JournalComputing
Volume106
Issue number2
DOIs
StatePublished - 20 Oct 2023

Keywords

  • Complex networks
  • Higher-order networks
  • Hypergraph algorithms
  • Network motifs

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