Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 27, Issue 3, Pages 503-515Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2017.1391696
Keywords
Motif detection; Sequential importance sampling; Subgraph concentration
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Funding
- National Science Foundation [DMS-1406455]
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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.
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