Common Structure Discovery in Collections of Bipartite Networks: Application to Pollination Systems

2025/12/01 by Louis Lacoste, Lacoste, Louis, Pierre Barbillon +3
Agricultural and Biological Sciences · Environmental Science · Physics and Astronomy · #Applications (stat.AP) #Complex Network Analysis Techniques #Ecology and Vegetation Dynamics Studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Plant and animal studies

paper · pdf · doi:10.48550/arxiv.2512.01716

openalex publication_date 2025/12/01 · openalex created_date 2025/12/03 · openalex updated_date 2026/07/28

Abstract

Bipartite networks are widely used to encode the ecological interactions. Being able to compare the organization of bipartite networks is a first step toward a better understanding of how environmental factors shape community structure and resilience. Yet current methods for structure detection in bipartite networks overlook shared patterns across collections of networks. We introduce the colBiSBM, a family of probabilistic models for collections of bipartite networks that extends the classical Latent Block Model (LBM). The proposed framework assumes that networks are independent realizations of a shared mesoscale structure, encoded through common inter-block connectivity parameters. We establish identifiability conditions for the different variants of colBiSBM and develop a variational EM algorithm for parameter estimation, coupled with an adaptation of the Integrated Classification Likelihood (ICL) criterion for model selection. We demonstrate how our approach can be used to classify networks based on their topology or organization. Simulation studies highlight the ability of colBiSBM to recover common structures, improve clustering performance, and enhance link prediction by borrowing strength across networks. An application to plant--pollinator networks highlights how the method uncovers shared ecological roles and partitions networks into sub-collections with similar connectivity patterns. These results illustrate the methodological and practical advantages of joint modeling over separate network analyses in the study of bipartite systems.

Citations

• From your Block to our Block: How to Find Shared Structure between Stochastic Block Models over Multiple Graphs
• Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
• Model-based clustering of multiple networks with a hierarchical algorithm
• Understanding graph embedding methods and their applications
• Inference for multiple heterogeneous networks with a common invariant subspace
• Inductive Representation Learning on Large Graphs
• Variational Graph Auto-Encoders
• Statistical clustering of temporal networks through a dynamic stochastic block model
• Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model

Related

• ‘ILSM': a package to analyze the interconnection structure of tripartite interaction networks
• The Robustness of Structural Features in Species Interaction Networks
• Reconstruction of plant–pollinator networks from observational data
• BiMAT: a MATLAB(R) package to facilitate the analysis and visualization of bipartite networks
• Inferring structure in bipartite networks using the latent block model and exact ICL