ArticleApplied network science2017
Efficient orbit-aware triad and quad census in directed and undirected graphs.
Article in Applied network science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- SAILoR: Structure-Aware Inference of Logic Rules.PloS one · 2024Article
- Host Recognition and Specific Infection ofJournal of fungi (Basel, Switzerland) · 2023Article
- Edge-colored directed subgraph enumeration on the connectome.Scientific reports · 2022Article
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Authors and funding
2 authors.
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Abstract
The prevalence of select substructures is an indicator of network effects in applications such as social network analysis and systems biology. Moreover, subgraph statistics are pervasive in stochastic network models, and they need to be assessed repeatedly in MCMC sampling and estimation algorithms. We present a new approach to count all induced and non-induced four-node subgraphs (the quad census) on a per-node and per-edge basis, complete with a separation into their non-automorphic roles in these subgraphs. It is the first approach to do so in a unified manner, and is based on only a clique-listing subroutine. Computational experiments indicate that, despite its simplicity, the approach outperforms previous, less general approaches. By way of the more presentable triad census, we additionally show how to extend the quad census to directed graphs. As a byproduct we obtain the asymptotically fastest triad census algorithm to date.
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