arXiv subjects

Zhaoyu Xing

Publications and source records attributed to Zhaoyu Xing.

5 recordsLinked to original sources

arXiv2026

NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity

Heterogeneous network data with rich nodal information become increasingly prevalent across multidisciplinary research, yet accurately modeling complex nodal heterogeneity and simultaneously selecting influential nodal attributes remains an open challenge. This problem is central to many applications in economics and sociology, when both nodal heterogeneity and high-dimensional individual characteristics highly affect network formation. We propose a statistically grounded, unified deep neural network approach for modeling nodal heterogeneity in random networks with high-dimensional nodal attributes, namely ``NetworkNet''. A key innovation of NetworkNet lies in a tailored neural architecture that explicitly parameterizes attribute-driven heterogeneity, and at the same time, embeds a scalable attribute selection mechanism. NetworkNet consistently estimates two types of latent heterogeneity functions, i.e., nodal expansiveness and popularity, while simultaneously performing data-driven attribute selection to extract influential nodal attributes. By unifying classical statistical network modeling with deep learning, NetworkNet delivers the expressive power of DNNs with methodological interpretability, algorithmic scalability, and statistical rigor with a non-asymptotic approximation error bound. Empirically, simulations demonstrate strong performance in both heterogeneity estimation and high-dimensional attribute selection. We further apply NetworkNet to a large-scale author-citation network among statisticians, revealing new insights into the dynamic evolution of research fields and scholarly impact.

stat.ME↗

arXiv2025

FLAT: Fused Lasso Regression with Adaptive Minimum Spanning Tree with Applications on Thermohaline Circulation

This article introduces a new methodology model both discrete and continuous spatial heterogeneity simultaneously with an application in detection of hyper-plain in thermohaline circulation. To enable the data-driven detection of spatial boundaries with heterogeneity, we constructs an adaptive minimum spanning tree guided by both spatial proximity and coefficient dissimilarity, and combines both a spatial fused regularization and LASSO-type regularization to estimate the spatial coefficients under the framework of spatial regression. Numerical simulations demonstrate the effectiveness of proposed method in both estimation and heterogeneity detection. The usefulness of the approach is further illustrated via an analysis of oceanic data that provides new empirical finds about Atlantic with detected surfaces in temperature-salinity relationship.

stat.ME↗

arXiv2025

Parallel Network Reconstruction with Multi-directional Regularization

Reconstructing large-scale latent networks from observed dynamics is crucial for understanding complex systems. However, the existing methods based on compressive sensing are often rendered infeasible in practice by prohibitive computational and memory costs. To address this challenge, we introduce a new distributed computing framework for efficient large-scale network reconstruction with parallel computing, namely PALMS (Parallel Adaptive Lasso with Multi-directional Signals). The core idea of PALMS is to decompose the complex global problem by partitioning network nodes, enabling the parallel estimation of sub-networks across multiple computing units. This strategy substantially reduces the computational complexity and storage requirements of classic methods. By using the adaptive multi-directional regularization on each computing unit, we also establish the consistency of PALMS estimator theoretically. Extensive simulation studies and empirical analyses on several large-scale real-world networks validate the computational efficiency and robust reconstruction accuracy of our approach.

math.ST↗

arXiv2025

GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering

It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algorithm combines both local geometric structure via manifold learning and global correlation structure of samples via regularized self-representation to select the discriminative features. The combination improves the accuracy of both feature selection and clustering by exploiting more comprehensive information. In addition, an iterative algorithm is proposed to solve the optimization problem and the convergency is proved. Simulations and two real data applications demonstrate the excellent finite-sample performance of GOLFS on both feature selection and clustering.

stat.ML↗

arXiv2025

Regularization and Selection in A Directed Network Model with Nodal Homophily and Nodal Effects

This article introduces a regularization and selection methods for directed networks with nodal homophily and nodal effects. The proposed approach not only preserves the statistical efficiency of the resulting estimator, but also ensures that the selection of nodal homophily and nodal effects is scalable with large-scale network data and multiple nodal features. In particular, we propose a directed random network model with nodal homophily and nodal effects, which includes the nodal features in the probability density of random networks. Subsequently, we propose a regularized maximum likelihood estimator with an adaptive LASSO-type regularizer. We demonstrate that the regularized estimator exhibits the consistency and possesses the oracle properties. In addition, we propose a network Bayesian information criterion which ensures the selection consistency while tuning the model. Simulation experiments are conducted to demonstrate the excellent numerical performance. An online friendship network among musicians with nodal musical preference is used to illustrate the usefulness of the proposed new network model in network-related empirical analysis.

stat.ME↗