Sampling motif-constrained ensembles of networks

Rico Fisher, Jorge C. Leitão, Tiago P. Peixoto, Eduardo G. Altmann

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

The statistical significance of network properties is conditioned on null models which satisfy specified properties but that are otherwise random. Exponential random graph models are a principled theoretical framework to generate such constrained ensembles, but which often fail in practice, either due to model inconsistency, or due to the impossibility to sample networks from them. These problems affect the important case of networks with prescribed clustering coefficient or number of small connected subgraphs (motifs). In this paper we use the Wang-Landau method to obtain a multicanonical sampling that overcomes both these problems. We sample, in polynomial time, networks with arbitrary degree sequences from ensembles with imposed motifs counts. Applying this method to social networks, we investigate the relation between transitivity and homophily, and we quantify the correlation between different types of motifs, finding that single motifs can explain up to 60% of the variation of motif profiles.
Original languageEnglish
Article number188701
JournalPhysical Review Letters
Volume115
Issue number18
DOIs
Publication statusPublished - 30 Oct 2015

Fingerprint Dive into the research topics of 'Sampling motif-constrained ensembles of networks'. Together they form a unique fingerprint.

Cite this