Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals

2025-09-19

AI Summary

This paper addresses model-free node clustering and parameter estimation for graphs generated by the stochastic block model (SBM). We propose a Lloyd-type iterative algorithm that makes no assumptions about the edge-weight distribution. Inspired by the k-means Lloyd heuristic, our approach is the first to extend this alternating optimization paradigm to graph-structured data: each iteration alternates between reassigning nodes based on current parameter estimates and updating parameters via sufficient statistics computed from the new partition. The method enjoys model independence, strong consistency guarantees, and high computational efficiency. In experiments on synthetic and real-world networks, it achieves clustering error comparable to state-of-the-art methods while running significantly faster. Applied to animal social networks, it successfully identifies interpretable social roles, providing behavioral ecologists with an efficient and robust model-free analytical tool.

Technology Category

Machine Learning: Graph-based Machine LearningData Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityReasoning under Uncertainty: Graphical Models

Application Category

Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semanticsWeb Mining and Content Analysis: Models for Web evolution

Abstract

We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in community detection. Drawing inspiration from the Lloyd algorithm for the $k$-means problem, our approach extends to SBMs with general edge weight distributions. We establish the consistency of our estimator under a natural identifiability condition. Through extensive numerical experiments, we benchmark our methods against state-of-the-art techniques, demonstrating significantly faster computation times with the lower order of estimation error. Finally, we validate the practical relevance of our algorithms by applying them to empirical network data from behavioral ecology.

Problem

• Develop model-free algorithms for SBM node clustering
• Achieve fast computation with low estimation error
• Apply to social role inference in animal networks

Innovation

• Model-free algorithms for SBM graphs
• Extends Lloyd algorithm to SBMs
• Faster computation with lower error