无需假设模型即可快速聚类社交网络节点，适用于动物社会角色分析。
Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
- 基于 Lloyd 算法思想，设计免模型的节点聚类方法。
- 在真实行为生态数据上实现更快计算与更低估计误差。
- 适合研究动物社会结构或大规模网络聚类的学者使用。
我们提出一类新型无模型算法，用于随机块模型（SBM）生成图中的节点聚类与参数推断，该模型是社区发现的基础框架。受 k-均值问题中 Lloyd 算法的启发，我们的方法可扩展至具有任意边权分布的 SBM。在自然可识别性条件下，我们证明了估计器的一致性。通过大量数值实验，将方法与现有先进技术对比，结果显示计算速度显著更快，且估计误差阶数更低。最后，我们在行为生态学的实证网络数据上验证了算法的实际适用性。
原文摘要 · Abstract (English)
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.
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