arXiv subjects

Zhixuan Shao

Publications and source records attributed to Zhixuan Shao.

3 recordsLinked to original sources

arXiv2026

Parametric Bootstrap for Fixed Edge-Probability Network Models

This paper studies parametric bootstrap methods for network data, with the goal of quantifying the uncertainty of network statistics of interest. While existing network resampling methods primarily focus on count statistics under node-exchangeable graphon models, we consider more general network statistics, including local statistics, under the Chung-Lu model without assuming node exchangeability. We show that the natural network parametric bootstrap, which first estimates the network-generating model and then draws bootstrap samples from the estimated model, generally suffers from bootstrap bias. As a general remedy, we show that a two-level bootstrap procedure provably reduces this bias. This extends the classical idea of the iterative bootstrap to the network setting, where the number of parameters grows with the network size. Moreover, for many network statistics, the second-level bootstrap provides a way to construct confidence intervals with higher accuracy. As a by-product of this analysis, we also obtain a central limit theorem for subgraph counts under the inhomogeneous Erdos-Rényi model, which may be of independent interest.

stat.ME↗

arXiv2025

On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction

The predictions of click through rate (CTR) and conversion rate (CVR) play a crucial role in the success of ad-recommendation systems. A Deep Hierarchical Ensemble Network (DHEN) has been proposed to integrate multiple feature crossing modules and has achieved great success in CTR prediction. However, its performance for CVR prediction is unclear in the conversion ads setting, where an ad bids for the probability of a user's off-site actions on a third party website or app, including purchase, add to cart, sign up, etc. A few challenges in DHEN: 1) What feature-crossing modules (MLP, DCN, Transformer, to name a few) should be included in DHEN? 2) How deep and wide should DHEN be to achieve the best trade-off between efficiency and efficacy? 3) What hyper-parameters to choose in each feature-crossing module? Orthogonal to the model architecture, the input personalization features also significantly impact model performance with a high degree of freedom. In this paper, we attack this problem and present our contributions biased to the applied data science side, including: First, we propose a multitask learning framework with DHEN as the single backbone model architecture to predict all CVR tasks, with a detailed study on how to make DHEN work effectively in practice; Second, we build both on-site real-time user behavior sequences and off-site conversion event sequences for CVR prediction purposes, and conduct ablation study on its importance; Last but not least, we propose a self-supervised auxiliary loss to predict future actions in the input sequence, to help resolve the label sparseness issue in CVR prediction. Our method achieves state-of-the-art performance compared to previous single feature crossing modules with pre-trained user personalization features.

cs.LG↗

arXiv2024

Determining the Number of Communities in Sparse and Imbalanced Settings

Community structures represent a crucial aspect of network analysis, and various methods have been developed to identify these communities. However, a common hurdle lies in determining the number of communities K, a parameter that often requires estimation in practice. Existing approaches for estimating K face two notable challenges: the weak community signal present in sparse networks and the imbalance in community sizes or edge densities that result in unequal per-community expected degree. We propose a spectral method based on a novel network operator whose spectral properties effectively overcome both challenges. This operator is a refined version of the non-backtracking operator, adapted from a "centered" adjacency matrix. Its leading eigenvalues are more concentrated than those of the adjacency matrix for sparse networks, while they also demonstrate enhanced signal under imbalance scenarios, a benefit attributed to the centering step. This is justified, either theoretically or numerically, under the null model K = 1, in both dense and ultra-sparse settings. A goodness-of-fit test based on the leading eigenvalue can be applied to determine the number of communities K.

stat.ME↗