论文标题
DIRICHLET网络分布的非核对结混合物用于分析股票所有权网络
Nondiagonal Mixture of Dirichlet Network Distributions for Analyzing a Stock Ownership Network
论文作者
论文摘要
块建模广泛用于复杂网络的研究。基石模型是在过去几十年中广泛使用的随机块模型（SBM）。但是，SBM在分析复杂网络时受到限制，因为该模型本质上是一个随机图模型，该模型无法再现许多复杂网络的基本属性，例如稀疏性和重尾分布。在本文中，我们提供了一个可交换的块模型，该模型结合了此类基本功能，并同时渗透给给定复杂网络的潜在块结构。我们的模型是贝叶斯非参数模型，可灵活地估计块数量并考虑到看不见的节点的可能性。使用一个合成数据集和一个现实世界的股票所有权数据集，我们表明我们的模型优于最先进的SBM，用于扣除链接预测任务。
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.
