23 citations · 49 across the 36 of their papers we have counts for
37 papers
Asymptotic Normality of the Hill Estimator in Linear Preferential Attachment
Tiandong Wang
We prove asymptotic normality of the Hill estimator based on the largest degrees in a homogeneous linear preferential attachment graph with vertices. Each new vertex send…
Pooling Mobility Obscures Epidemic Invasion Routes
Tiandong Wang, Wei Yang
Epidemic models often pool air travel, commuting, and other mobility layers into a single weighted network. Pooling keeps the total imported infections into a region but discards t…
Hub Neighbor-Degree Diagnostics for Sparse Random Graphs
Qian Hui, Tiandong Wang
Networks with nearly identical degree distributions can place their hubs in sharply different neighborhoods. We develop a model diagnostic based on the mean degree of the neighbors…
Efficient Topic Model Estimation under Heavy-Tailed Document Lengths
Daniel Cirkovic, Tiandong Wang
Early inquiries into the statistical properties of natural language found that words tend to occur with power-law frequencies. This observation, closely associated with Zipf's law,…
Spatial Dependence in Directed Preferential-Attachment Networks
Zihan Li, Tiandong Wang
Spatially embedded directed networks, such as airline networks, often exhibit simultaneous high activity at nearby nodes. Preferential attachment (PA) explains hub dominance. We ex…
A Multilayer Probit Network Model for Community Detection with Dependent Edges and Layers
Dapeng Shi, Haoran Zhang, Tiandong Wang +1
Community detection in multilayer networks, which aims to identify groups of nodes exhibiting similar connectivity patterns across multiple network layers, has attracted considerab…
Structural Causal Models for Extremes: an Approach Based on Exponent Measures
Shuyang Bai, Fei Fang, Tiandong Wang
We introduce a new formulation of structural causal models for extremes, called the extremal structural causal model (eSCM). Unlike conventional structural causal models, where ran…
Classification of Extremal Dependence in Financial Markets via Bootstrap Inference
Qian Hui, Sidney I. Resnick, Tiandong Wang
Accurately identifying the extremal dependence structure in multivariate heavy-tailed data is a fundamental yet challenging task, particularly in financial applications. Following…
Systemic Risk Management via Maximum Independent Set in Extremal Dependence Networks
Qian Hui, Tiandong Wang
The failure of key financial institutions may accelerate risk contagion due to their interconnections within the system. In this paper, we propose a robust portfolio strategy to mi…
Measuring Interlayer Dependence of Large Degrees in Multilayer Inhomogeneous Random Graphs
Zhuoye Han, Tiandong Wang
Accurately capturing interlayer dependence is essential for understanding the structure of complex multilayer networks. We propose an upper tail dependence estimator specifically d…
On tail inference in scale-free inhomogeneous random graphs
Daniel Cirkovic, Tiandong Wang, Daren B. H. Cline
Both empirical and theoretical investigations of scale-free network models have found that large degrees in a network exert an outsized impact on its structure. However, the tools…
Mitigating Extremal Risks: A Network-Based Portfolio Strategy
Qian Hui, Tiandong Wang
In financial markets marked by inherent volatility, extreme events can result in substantial investor losses. This paper proposes a portfolio strategy designed to mitigate extremal…
Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models
Dapeng Shi, Tiandong Wang, Zhiliang Ying
Exploring and detecting community structures hold significant importance in genetics, social sciences, neuroscience, and finance. Especially in graphical models, community detectio…
Emergence of Multivariate Extremes in Multilayer Inhomogeneous Random Graphs
Daniel Cirkovic, Tiandong Wang, Daren B. H. Cline
In this paper, we propose a multilayer inhomogeneous random graph model (MIRG), whose layers may consist of both single-edge and multi-edge graphs. In the single layer case, it has…
2RV+HRV and Testing for Strong VS Full Dependence
Tiandong Wang, Sidney I. Resnick
Preferential attachment models of network growth are bivariate heavy tailed models for in- and out-degree with limit measures which either concentrate on a ray of positive slope fr…
Multilayer Network Regression with Eigenvector Centrality and Community Structure
Zhuoye Han, Tiandong Wang, Zhiliang Ying
In the analysis of complex networks, centrality measures and community structures play pivotal roles. For multilayer networks, a critical challenge lies in effectively integrating…
Modeling Random Networks with Heterogeneous Reciprocity
Daniel Cirkovic, Tiandong Wang
Reciprocity, or the tendency of individuals to mirror behavior, is a key measure that describes information exchange in a social network. Users in social networks tend to engage in…
Generating General Preferential Attachment Networks with R Package wdnet
Yelie Yuan, Tiandong Wang, Jun Yan +1
Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their…
Random Networks with Heterogeneous Reciprocity
Tiandong Wang, Sidney Resnick
Users of social networks display diversified behavior and online habits. For instance, a user's tendency to reply to a post can depend on the user and the person posting. For conve…
Likelihood-based Inference for Random Networks with Changepoints
Daniel Cirkovic, Tiandong Wang, Xianyang Zhang
Generative, temporal network models play an important role in analyzing the dependence structure and evolution patterns of complex networks. Due to the complicated nature of real n…
EVIboost for the Estimation of Extreme Value Index under Heterogeneous Extremes
Jiaxi Wang, Yanxi Hou, Xingchi Li +1
Modeling heterogeneity on heavy-tailed distributions under a regression framework is challenging, and classical statistical methodologies usually place conditions on the distributi…
Preferential Attachment with Reciprocity: Properties and Estimation
Daniel Cirkovic, Tiandong Wang, Sidney Resnick
Reciprocity in social networks helps understand information exchange between two individuals, and indicates interaction patterns between pairs of users. A recent study indicates th…
An Efficient Algorithm for Generating Directed Networks with Predetermined Assortativity Measures
Tiandong Wang, Jun Yan, Yelie Yuan +1
Assortativity coefficients are important metrics to analyze both directed and undirected networks. In general, it is not guaranteed that the fitted model will always agree with the…
Asymptotic Dependence of In- and Out-Degrees in a Preferential Attachment Model with Reciprocity
Tiandong Wang, Sidney I. Resnick
Reciprocity characterizes the information exchange between users in a network, and some empirical studies have revealed that social networks have a high proportion of reciprocal ed…