Estimating Mixed Memberships With Sharp Eigenvector Deviations
نویسندگان
چکیده
منابع مشابه
Estimating Mixed Memberships with Sharp Eigenvector Deviations
We consider the problem of estimating overlapping community memberships. Existing provable algorithms for this problem either make strong assumptions about the population [33, 16], or are too computationally expensive [3, 15]. We work under the popular Mixed Membership Stochastic Blockmodel (MMSB) [2]. Using the inherent geometry of this model, we link the inference of overlapping communities t...
متن کاملFinding Mixed-Memberships in Social Networks
This paper addresses the problem of unsupervised group discovery in social networks. We adopt a nonparametric Bayesian framework that extends previous models to networks where the interacting objects can simultaneously belong to several groups (i.e., mixed membership). For this purpose, a hierarchical nonparametric prior is utilized and inference is performed using Gibbs sampling. The resulting...
متن کاملEstimating true standard deviations
A basic statistic that students learn about in their classes is the standard deviation. Like any statistic, standard deviations are influenced by systematic factors and randomness. I propose that researchers should report “corrected” or “true” standard deviations and I show how to calculate them. The notion of “true” statistics comes out of classical test theory (see Lord and Novick, 1968; Gull...
متن کاملSharp Large Deviations for Gaussian Quadratic Forms with Applications
Under regularity assumptions, we establish a sharp large deviation principle for Hermitian quadratic forms of stationary Gaussian processes. Our result is similar to the well-known Bahadur-Rao theorem [2] on the sample mean. We also provide several examples of application such as the sharp large deviation properties of the Neyman-Pearson likelihood ratio test, of the sum of squares, of the Yule...
متن کاملOn Mixed Memberships and Symmetric Nonnegative Matrix Factorizations
The problem of finding overlapping communities in networks has gained much attention recently. Optimization-based approaches use nonnegative matrix factorization (NMF) or variants, but the global optimum cannot be provably attained in general. Model-based approaches, such as the popular mixed membership stochastic blockmodel or MMSB (Airoldi et al., 2008), use parameters for each node to specif...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2020
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2020.1751645