An Efficient Sampling Algorithm for Network Motif Detection

Yinghan Chen, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


We propose a sequential importance sampling strategy to estimate subgraph frequencies and detect network motifs. The method is developed by sampling subgraphs sequentially node by node using a carefully chosen proposal distribution. Viewing the subgraphs as rooted trees, we propose a recursive formula that approximates the number of subgraphs containing a particular node or set of nodes. The proposal used to sample nodes is proportional to this estimated number of subgraphs. The method generates subgraphs from a distribution close to uniform, and performs better than competing methods. We apply the method to four real-world networks and demonstrate outstanding performance in practical examples. Supplemental materials for the article are available online.

Original languageEnglish (US)
Pages (from-to)503-515
Number of pages13
JournalJournal of Computational and Graphical Statistics
Issue number3
StatePublished - Jul 3 2018


  • Motif detection
  • Sequential importance sampling
  • Subgraph concentration

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty


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