TY - JOUR
T1 - Community detection and reciprocity in networks by jointly modelling pairs of edges
AU - Contisciani, Martina
AU - Safdari, Hadiseh
AU - De Bacco, Caterina
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - To unravel the driving patterns of networks, the most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce the structural features of the network. Therefore, attempts are always made to develop models that incorporate these network properties beside the community structure. In this article, we present a probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks. Our approach jointly models pairs of edges with exact two-edge joint distributions. In addition, it provides closed-form analytical expressions for both marginal and conditional distributions. We validate our model on synthetic data in recovering communities, edge prediction tasks and generating synthetic networks that replicate the reciprocity values observed in real networks. We also highlight these findings on two real datasets that are relevant for social scientists and behavioural ecologists. Our method overcomes the limitations of both standard algorithms and recent models that incorporate reciprocity through a pseudo-likelihood approximation. The inference of the model parameters is implemented by the efficient and scalable expectation-maximization algorithm, as it exploits the sparsity of the dataset. We provide an open-source implementation of the code online.
AB - To unravel the driving patterns of networks, the most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce the structural features of the network. Therefore, attempts are always made to develop models that incorporate these network properties beside the community structure. In this article, we present a probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks. Our approach jointly models pairs of edges with exact two-edge joint distributions. In addition, it provides closed-form analytical expressions for both marginal and conditional distributions. We validate our model on synthetic data in recovering communities, edge prediction tasks and generating synthetic networks that replicate the reciprocity values observed in real networks. We also highlight these findings on two real datasets that are relevant for social scientists and behavioural ecologists. Our method overcomes the limitations of both standard algorithms and recent models that incorporate reciprocity through a pseudo-likelihood approximation. The inference of the model parameters is implemented by the efficient and scalable expectation-maximization algorithm, as it exploits the sparsity of the dataset. We provide an open-source implementation of the code online.
KW - community detection
KW - latent variables
KW - network analysis
KW - probabilistic generative models
KW - reciprocity
UR - http://www.scopus.com/inward/record.url?scp=85136239169&partnerID=8YFLogxK
U2 - 10.1093/comnet/cnac034
DO - 10.1093/comnet/cnac034
M3 - Article
AN - SCOPUS:85136239169
SN - 2051-1310
VL - 10
JO - Journal of Complex Networks
JF - Journal of Complex Networks
IS - 4
M1 - cnac034
ER -