Block modeling is widely used in studies on complex networks. The cornerstone model is the stochastic block model (SBM), widely used over the past decades. However, the SBM is limited in analyzing complex networks as the model is, in essence, a random graph model that cannot reproduce the basic properties of many complex networks, such as sparsity and heavy-tailed degree distribution. In this paper, we provide an edge exchangeable block model that incorporates such basic features and simultaneously infers the latent block structure of a given complex network. Our model is a Bayesian nonparametric model that flexibly estimates the number of blocks and takes into account the possibility of unseen nodes. Using one synthetic dataset and one real-world stock ownership dataset, we show that our model outperforms state-of-the-art SBMs for held-out link prediction tasks.

中文翻译：

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用于分析股权网络的狄利克雷网络分布的非对角混合

块建模广泛用于复杂网络的研究。基石模型是随机块模型 (SBM)，在过去几十年中被广泛使用。然而，SBM 在分析复杂网络方面存在局限性，因为该模型本质上是一种随机图模型，无法再现许多复杂网络的基本属性，例如稀疏性和重尾度分布。在本文中，我们提供了一个边缘可交换块模型，该模型结合了这些基本特征，并同时推断给定复杂网络的潜在块结构。我们的模型是一个贝叶斯非参数模型，它灵活地估计了块的数量并考虑了不可见节点的可能性。使用一个合成数据集和一个真实世界的股权数据集，