arXiv subjects

Owen G. Ward

Publications and source records attributed to Owen G. Ward.

10 recordsLinked to original sources

arXiv2026

Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations

We study continuous-time relational event data, where time-stamped dyadic interactions reflect both individual node propensities and evolving relational proximity. We propose a dynamic latent space model for inhomogeneous Poisson processes, where event intensities depend on node-specific activity parameters and time-varying latent distances modeled via flexible B-splines. We prove model identifiability by decoupling baseline activity from latent position, ensuring high interaction volumes do not warp the spatial map. For scalability, we develop a minibatch stochastic gradient algorithm with stable initialization and geometric anchoring, alongside an effective-degrees-of-freedom BIC for tuning model complexity. Simulations confirm accurate parameter recovery and out-of-sample prediction. Applied to cooperative diplomatic events among 60 major economies (1995--2022), the model uncovers shifting patterns of international cooperation and isolates mobile geopolitical actors from stationary institutional anchors.

stat.AP↗

arXiv2026

Bayesian Modeling for Aggregated Relational Data: A Unified Perspective

Aggregated relational data is widely collected to study social networks, in fields such as sociology, public health and economics. Many of the successes of ARD inference have been driven by increasingly complex Bayesian models, which provide principled and flexible ways of reflecting dependence patterns and biases encountered in real data. In this work we provide researchers with a unified collection of Bayesian implementations of existing models for ARD, within the state-of-the-art Bayesian sampling language Stan. Our implementations incorporate within-iteration rescaling procedures by default, improving algorithm run time and convergence diagnostics. Estimating ARD parameters requires carefully balancing model complexity against computational cost and data requirements, yet this trade-off has received relatively limited systematic attention in the literature. Moreover, general model comparison tools applicable across a wide range of ARD models remain underdeveloped, and existing approaches often require substantial expertise in Bayesian computation and software. Using synthetic data, we demonstrate how well competing models recover true personal network sizes and subpopulation sizes and how existing posterior predictive checks compare across a range of Bayesian ARD models. We provide code to leverage Stan's modeling framework for exact $K$-fold cross-validation, and explain why approximate leave-one-out estimates often fail for many ARD models. This work highlights important connections and future directions in Bayesian modeling of ARD, providing practical guidance for selecting and evaluating Bayesian ARD models.

stat.ME↗

arXiv2026

Estimating Complex Densities using Two-Stage Normalizing Flows

In many scientific applications, the target probability distribution cannot be evaluated in closed form or sampled from directly. Instead, it can often be decomposed into multiple components, some of which are accessible only through samples generated by simulators or external datasets, while others admit tractable mathematical expressions or are specified through statistical assumptions about variable relationships. Developing inference methods that coherently integrate these heterogeneous sources of information remains an open challenge. In this paper, we propose a Two-Stage Normalizing Flows framework for approximating and sampling from such distributions. The method first learns the densities of components for which only samples are available, and then combines the outputs with the analytically specified terms to reconstruct the full target distribution in a second stage. The resulting model enables both point-wise density evaluation and efficient generation of representative samples, without requiring direct access to the full target density or joint samples from the complete model. We assess the proposed approach through simulation studies in joint density inference and Bayesian hierarchical models with inaccessible likelihoods. The proposed framework is able to accurately recover complex, highly nonlinear target structures using only partial information about the target density, providing stable and flexible approximations in settings where standard modeling assumptions do not hold (or when complete access to the target distribution is not available). Analysis of a large scale astronomy application highlights interesting differences between our method and existing approaches. Our normalizing flows procedure offers a robust and flexible approach to inference for intractable target distributions across both simulated and real-world applications.

stat.ME↗

arXiv2025

Investigating Experiential Effects in Online Chess using a Hierarchical Bayesian Analysis

The presence or absence of winner-loser effects is a widely discussed phenomenon across both sports and psychology research. Investigation of such effects is often hampered by the limited availability of data. Online chess has exploded in popularity in recent years and provides vast amounts of data which can be used to explore this question. With a hierarchical Bayesian regression model, we carefully investigate the presence of such experiential effects in online chess. Using a large quantity of online chess data, we see little evidence for experiential effects that are consistent across all players, with some individual players showing some evidence for such effects. Given the challenging temporal nature of this data, we discuss several methods for assessing the suitability of our model and carefully check its validity.

stat.AP↗

arXiv2024

Community Detection Guarantees Using Embeddings Learned by Node2Vec

Embedding the nodes of a large network into an Euclidean space is a common objective in modern machine learning, with a variety of tools available. These embeddings can then be used as features for tasks such as community detection/node clustering or link prediction, where they achieve state of the art performance. With the exception of spectral clustering methods, there is little theoretical understanding for commonly used approaches to learning embeddings. In this work we examine the theoretical properties of the embeddings learned by node2vec. Our main result shows that the use of $k$-means clustering on the embedding vectors produced by node2vec gives weakly consistent community recovery for the nodes in (degree corrected) stochastic block models. We also discuss the use of these embeddings for node and link prediction tasks. We demonstrate this result empirically, and examine how this relates to other embedding tools for network data.

stat.ML↗

arXiv2024

Scalable Community Detection in Massive Networks Using Aggregated Relational Data

The mixed membership stochastic blockmodel (MMSB) is a popular Bayesian network model for community detection. Fitting such large Bayesian network models quickly becomes computationally infeasible when the number of nodes grows into hundreds of thousands and millions. In this paper we propose a novel mini-batch strategy based on aggregated relational data that leverages nodal information to fit MMSB to massive networks. We describe a scalable inference method that can utilize nodal information that often accompanies real-world networks. Conditioning on this extra information leads to a model that admits a parallel stochastic variational inference algorithm, utilizing stochastic gradients of bipartite graph formed from aggregated network ties between node subpopulations. We apply our method to a citation network with over two million nodes and 25 million edges, capturing explainable structure in this network. Our method recovers parameters and achieves better convergence on simulated networks generated according to the MMSB.

cs.SI↗

arXiv2023

Online Estimation and Community Detection of Network Point Processes for Event Streams

A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, the true connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the dynamic temporal component. A natural way to take account of these temporal dynamics of interactions is to use point processes as the foundation of network models for community detection. Computational complexity hampers the scalability of such approaches to large sparse networks. To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models. We describe this procedure for networks models capturing community structure. This structure can be learned as new events are observed on the network, updating the inferred community assignments. We investigate the theoretical properties of such an inference scheme, and provide regret bounds on the loss function of this procedure. The proposed inference procedure is then thoroughly compared, using both simulation studies and real data, to non-online variants. We demonstrate that online inference can obtain comparable performance, in terms of community recovery, to non-online variants, while realising computational gains. Our proposed inference framework can also be readily modified to incorporate other popular network structures.

cs.SI↗

arXiv2022

Network Hawkes Process Models for Exploring Latent Hierarchy in Social Animal Interactions

Group-based social dominance hierarchies are of essential interest in animal behavior research. Studies often record aggressive interactions observed over time, and models that can capture such dynamic hierarchy are therefore crucial. Traditional ranking methods summarize interactions across time, using only aggregate counts. Instead, we take advantage of the interaction timestamps, proposing a series of network point process models with latent ranks. We carefully design these models to incorporate important characteristics of animal interaction data, including the winner effect, bursting and pair-flip phenomena. Through iteratively constructing and evaluating these models we arrive at the final cohort Markov-Modulated Hawkes process (C-MMHP), which best characterizes all aforementioned patterns observed in interaction data. We compare all models using simulated and real data. Using statistically developed diagnostic perspectives, we demonstrate that the C-MMHP model outperforms other methods, capturing relevant latent ranking structures that lead to meaningful predictions for real data.

stat.AP↗

arXiv2021

Next Waves in Veridical Network Embedding

Embedding nodes of a large network into a metric (e.g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences. Generally, a representation of a network object is learned in a Euclidean geometry and is then used for subsequent tasks regarding the nodes and/or edges of the network, such as community detection, node classification and link prediction. Network embedding algorithms have been proposed in multiple disciplines, often with domain-specific notations and details. In addition, different measures and tools have been adopted to evaluate and compare the methods proposed under different settings, often dependent of the downstream tasks. As a result, it is challenging to study these algorithms in the literature systematically. Motivated by the recently proposed Veridical Data Science (VDS) framework, we propose a framework for network embedding algorithms and discuss how the principles of predictability, computability and stability apply in this context. The utilization of this framework in network embedding holds the potential to motivate and point to new directions for future research.

stat.ML↗

arXiv2021

Markov-Modulated Hawkes Processes for Sporadic and Bursty Event Occurrences

Modeling event dynamics is central to many disciplines. Patterns in observed event arrival times are commonly modeled using point processes. Such event arrival data often exhibits self-exciting, heterogeneous and sporadic trends, which is challenging for conventional models. It is reasonable to assume that there exists a hidden state process that drives different event dynamics at different states. In this paper, we propose a Markov Modulated Hawkes Process (MMHP) model for learning such a mixture of event dynamics and develop corresponding inference algorithms. Numerical experiments using synthetic data demonstrate that MMHP with the proposed estimation algorithms consistently recover the true hidden state process in simulations, while email data from a large university and data from an animal behavior study show that the procedure captures distinct event dynamics that reveal interesting social structures in the real data.

stat.AP↗