摘要翻译:
在网络分析的许多应用中,区分影响网络结构的观察因素和未观察因素是很重要的。为此,我们开发了随机块模型中未观测块和协变量影响的谱估计器。在理论方面,我们建立了估计量的渐近正态性,以便进行后续的推理。在应用方面,我们表明计算我们的估计量比标准的变分期望--最大化算法快得多,并且对大型网络有很好的规模。Monte Carlo实验表明,该估计器在不同的数据生成过程下都具有良好的性能。我们对脸书数据的应用显示了性别、角色和校园居住地的同源性证据,同时允许我们发现未被观察到的社区。本文的结果为谱估计观测到的协变量和未观测到的潜在社区结构对网络链路形成概率的影响提供了基础。
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英文标题:
《Spectral inference for large Stochastic Blockmodels with nodal
covariates》
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作者:
Angelo Mele and Lingxin Hao and Joshua Cape and Carey E. Priebe
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最新提交年份:
2021
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分类信息:
一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Statistics 统计学
二级分类:Computation 计算
分类描述:Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类:Statistics 统计学
二级分类:Machine Learning 机器学习
分类描述:Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文(监督,无监督,半监督学习,图形模型,强化学习,强盗,高维推理等)与统计或理论基础
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英文摘要:
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. To this end, we develop spectral estimators for both unobserved blocks and the effect of covariates in stochastic blockmodels. On the theoretical side, we establish asymptotic normality of our estimators for the subsequent purpose of performing inference. On the applied side, we show that computing our estimator is much faster than standard variational expectation--maximization algorithms and scales well for large networks. Monte Carlo experiments suggest that the estimator performs well under different data generating processes. Our application to Facebook data shows evidence of homophily in gender, role and campus-residence, while allowing us to discover unobserved communities. The results in this paper provide a foundation for spectral estimation of the effect of observed covariates as well as unobserved latent community structure on the probability of link formation in networks.
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PDF链接:
https://arxiv.org/pdf/1908.06438