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存在不依从与未知干扰时的因果推断AI 解读

We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of network interference is unknown to researchers. To estimate meaningful causal parameters in this situation, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate…

双边关系数据核密度估计的一致推断AI 解读

Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimax-optimal uniform convergence rate of the dyadic…

用于高维回归的因子增强稀疏直通深度 ReLU 神经网络AI 解读

This paper introduces a Factor Augmented Sparse Throughput (FAST) model that utilizes both latent factors and sparse idiosyncratic components for nonparametric regression. The FAST model bridges factor models on one end and sparse nonparametric models on the other end. It encompasses structured nonparametric models such as factor augmented additive models and sparse low-dimensional nonparametric interaction models and covers the cases where the covariates do not admit factor…

超图数据的潜在空间建模AI 解读

The increasing prevalence of relational data describing interactions among a target population has motivated a wide literature on statistical network analysis.In many applications, interactions may involve more than two members of the population and this data is more appropriately represented by a hypergraph.In this article, we present a model for hypergraph data that extends the well-established latent space approach for graphs and, by drawing a connection to constructs…

强干扰实验的新估计目标AI 解读

In experiments that study social phenomena, such as peer influence or herd immunity, the treatment of one unit may influence the outcomes of others. Such “interference between units” violates traditional approaches for causal inference, so that additional assumptions are often imposed to model or limit the underlying social mechanism. For binary outcomes, we propose new estimands that can be estimated without such assumptions, allowing for interval estimates that assume only…

存在隐蔽混杂的因果网络发现与推断AI 解读

This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental…

部分线性空间自回归模型的模型检验AI 解读

This article proposes two new classes of nonparametric tests for the correct specification of linear spatial autoregressive models based on the “integrated conditional moment” approach. Our test statistics are constructed as continuous functionals of a residual marked empirical process as well as its projected version. We derive asymptotic properties of the test statistics under the null hypothesis, the alternative hypothesis, and a sequence of local alternatives. The…

空间自回归模型的一致非高斯伪极大似然估计AI 解读

This paper studies the non-Gaussian pseudo maximum likelihood (PML) estimation of a spatial autoregressive (SAR) model with SAR disturbances. If the spatial weights matrix $M_{n}$ for the SAR disturbances is normalized to have row sums equal to 1 or the model reduces to a SAR model with no SAR process of disturbances, the non-Gaussian PML estimator (NGPMLE) for model parameters except the intercept term and the variance $\sigma _{0}^{2}$ of independent and identically…

对《网络潜在增长过程的根节点与社群推断》的致谢动议附议及讨论AI 解读

Patrick Rubin-Delanchy; Seconder of the vote of thanks to Crane and Xu and contribution to the Discussion of `Root and community inference on the latent gr

Feng 与 Sun 对“网络潜在增长过程的根与社群推断”的讨论AI 解读

We congratulate Drs. Crane and Xu for their fascinating piece of work. In the multiple roots PAPER model, one assumption is that different trees share the same affine preferential attachment (APA) growth parameters (α,β)⁠. In practice, the multiple trees in a network may have varying growth mechanisms, and it may be of interest to model each tree to have its own growth parameters. We consider such a heterogeneous affine preferential attachment (HAPA) model. Definition 1…

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范旺、余怡与里纳尔多对网络潜在生长过程根与社区推断的讨论AI 解读

We congratulate Prof. Crane and Dr Xu for introducing a very interesting and appealing statistical model for Markovian network growth. The PAPER model enjoys several qualities. In particular, (i) it strikes a rare balance between analytic elegance and tractability, and expressive power; (ii) it allows for practicable algorithms for statistical inference that scale to networks of very large size, and (iii) it is very flexible and can be readily generalized to model a variety…

Andrej Srakar 对《网络潜在增长过程的根节点与社区推断》的讨论评论AI 解读

Paper presented by Harry Crane and Min Xu develops a rather novel area of network archaeology, which builds on findings of network science, probability theory and (in Crane and Xu's article) Bayesian statistics. My comments are directed to possible extensions for future work in this promising area of research. Firstly, in terms of network stochastic processes (authors use a combination of preferential attachment and Erdős–Rényi models) paper does little to explain…

Catalano等对《网络潜在生长过程的根与社群推断》的讨论AI 解读

We congratulate the authors for their methodological and theoretical contribution to the statistical literature on networks. A natural extension of the proposed PAPER model is included, with K communities growing simultaneously and where new nodes are either assigned to an existing community or elected as a new root. The employed assignment rule is of Pólya-urn type, which leads to a logarithmic growth of the number of communities (Korwar & Hollander, 1973) and is known to…

对 Crane 与 Xu《网络潜在生长过程的根与社区推断》的致谢与讨论AI 解读

We commend the authors on an illuminating article which significantly extends the state of the art in network archaeology. Notably, whereas most other literature in the area has provided methods for root inference which are theoretically grounded only in the case of random trees, the techniques in this paper have been designed to model nontree-structured networks, as well. This has important practical implications, as many real-world networks contain one or more cycles. The…

杨青与童昕对“网络潜在增长过程的根节点与社区推断”的讨论AI 解读

First, we congratulate Dr. Crane and Dr. Xu for this important contribution to statistical network analysis. To address an overlooked fact that most real networks are formed through a growth process, the authors introduce a useful PAPER model and its variations and skilfully construct computationally feasible confidence sets for the root node(s). In the data analysis, the authors also use their models as a new approach to community detection. On this note, our interest lies…

Wyse 等对《网络潜在增长过程的根节点与社区推断》的讨论AI 解读

We congratulate the authors on a thought provoking paper introducing innovative ideas for the statistical modelling of networks. The emphasis on identifying highly probable root nodes is both original and intriguing. In particular, using the root node credible set methods for node clustering within the PAPER(α,β,α0,θ) random K model (Section 6.3) is an interesting perspective. We wonder how these approaches might connect with latent space model-based node clustering methods…

江一聪与柯征对《网络潜在增长过程的根节点与社群推断》的讨论AI 解读

We congratulate the authors on an excellent paper! Crane and Xu (2021) proposed novel methods for finding ‘root nodes’ from a single snapshot of a dynamic network process, with several interesting real-data examples. We now consider a new application for finding ‘root papers’ in a citation network. The MADStat dataset (Ji et al., 2022; Ke et al., 2023) consists of the bibtex and citation information of over 83 K papers, which we use to construct paper citation networks.…

Sayan Banerjee 对《网络潜在生长过程的根与社区推断》的讨论AI 解读

I congratulate the authors on their elegant and computationally tractable approach to inference problems in network archaeology. This is one of the few papers which rigorously analyses inference for noisy temporal networks. The key idea is to compute the distribution of the possible arrival orders of vertices given an unlabelled network realization (online detection) and construct the confidence set for the root by sorting vertices according to their probability of arriving…

Tianxi Li 对《网络潜在生长过程的根节点与社群推断》的讨论AI 解读

I want to congratulate Prof. Crane and Prof. Xu for their impressive work. The paper introduces two novel components: the PAPER model and the inference framework for the tree. Both of them bring many insights into handling complex network data and open the doors to new problems. I will discuss extension problems from both aspects. The PAPER model assumes preferential attachment trees with additional the Erdős-Rényi (ER) edges. One nice property of the model lies in its…

Ascolani、Lijoi 与 Prünster 对《网络潜在生长过程的根与社群推断》的讨论AI 解读

We congratulate the authors for their timely and insightful contribution, which introduces a novel Bayesian approach to inferring the latent structure (early history and community detection) given a current observation of a network with n nodes. The Bayesian component of the model is summarized through a uniform prior on the random relabelling associated to the observed network. The methodological innovation is remarkably complemented by strong theoretical (frequentist)…

网络潜在生长过程的根节点与社区推断AI 解读

Abstract Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erdős–Rényi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdős–Rényi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component…

利用高阶结构进行网络社区检测AI 解读

Summary In many real-world networks, it is often observed that subgraphs or higher-order structures of certain configurations, e.g., triangles and by-fans, are overly abundant compared to standard randomly generated networks (Milo et al., 2002). However, statistical models accounting for this phenomenon are limited, especially when community structure is of interest. This limitation is coupled with a lack of community detection methods that leverage subgraphs or higher-order…

局部网络统计量的中心极限定理AI 解读

Summary Subgraph counts, in particular the number of occurrences of small shapes such as triangles, characterize properties of random networks. As a result, they have seen wide use as network summary statistics. Subgraphs are typically counted globally, making existing approaches unable to describe vertex-specific characteristics. In contrast, rooted subgraphs focus on vertex neighbourhoods, and are fundamental descriptors of local network properties. We derive the…