TY - JOUR
T1 - Flexible inference in heterogeneous and attributed multilayer networks
AU - Contisciani, Martina
AU - Hobbhahn, Marius
AU - Power, Eleanor A
AU - Hennig, Philipp
AU - Bacco, Caterina De
N1 - © The Author(s) 2025. Published by Oxford University Press on behalf of National Academy of Sciences.
PY - 2025/1
Y1 - 2025/1
N2 - Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this article, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information. Our approach employs a Bayesian framework combined with the Laplace matching technique to ease interpretation of inferred parameters. Furthermore, the algorithmic implementation relies on automatic differentiation, avoiding the need for explicit derivations. This makes our model scalable and flexible to adapt to any combination of input data. We demonstrate the effectiveness of our method in detecting overlapping community structures and performing various prediction tasks on heterogeneous multilayer data, where nodes and edges have different types of attributes. Additionally, we showcase its ability to unveil a variety of patterns in a social support network among villagers in rural India by effectively utilizing all input information in a meaningful way.
AB - Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this article, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information. Our approach employs a Bayesian framework combined with the Laplace matching technique to ease interpretation of inferred parameters. Furthermore, the algorithmic implementation relies on automatic differentiation, avoiding the need for explicit derivations. This makes our model scalable and flexible to adapt to any combination of input data. We demonstrate the effectiveness of our method in detecting overlapping community structures and performing various prediction tasks on heterogeneous multilayer data, where nodes and edges have different types of attributes. Additionally, we showcase its ability to unveil a variety of patterns in a social support network among villagers in rural India by effectively utilizing all input information in a meaningful way.
UR - https://doi.org/10.1093/pnasnexus/pgaf005
U2 - 10.1093/pnasnexus/pgaf005
DO - 10.1093/pnasnexus/pgaf005
M3 - Article
C2 - 39850077
SN - 2752-6542
VL - 4
JO - PNAS Nexus
JF - PNAS Nexus
IS - 1
M1 - pgaf005
ER -