Duy Vu

The dynamics of health behavior sentiments on a large online social network

Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent.

Quality of Care and Interhospital Collaboration: A study of Patient Transfers in Italy

Objectives:We examine the dynamics of patient-sharing relations within an Italian regional community of 35 hospitals serving approximately 1,300,000 people. We test whether interorganizational relations provide individual patients access to higher quality providers of care. Research Design and Methods:We reconstruct the complete temporal sequence of the 3461 consecutive interhospital patient-sharing events observed between each pair of hospitals in the community during 2005–2008. We distinguish between transfers occurring between and within different medical specialties. We estimate newly derived models for relational event sequences that allow us to control for the most common forms of network-like dependencies that are known to characterize collaborative relations between hospitals. We use 45-day risk-adjusted readmission rate as a proxy for hospital quality. Results:After controls (eg, geographical distance, size, and the existence of prior collaborative relations), we find that patients flow from less to more capable hospitals. We show that this result holds for patient being shared both between as well as within medical specialties. Nonetheless there are strong and persistent other organizational and relational effects driving transfers. Conclusions:Decentralized patient-sharing decisions taken by the 35 hospitals give rise to a system of collaborative interorganizational arrangements that allow the patient to access hospitals delivering a higher quality of care. This result is relevant for health care policy because it suggests that collaborative relations between hospitals may produce desirable outcomes both for individual patients, and for regional health care systems.

Differential Privacy for Clinical Trial Data: Preliminary Evaluations

The concept of differential privacy as a rigorous definition of privacy has emerged from the cryptographic community. However, further careful evaluation is needed before we can apply these theoretical results to privacy preservation in everyday data mining and statistical analysis. In this paper we demonstrate how to integrate a differential privacy framework with the classical statistical hypothesis testing in the domain of clinical trials where personal information is sensitive. We develop concrete methodology that researchers can use. We derive rules for the sample size adjustment whereby both statistical efficiency and differential privacy can be achieved for the specific tests for binomial random variables and in contingency tables.

Model-based clustering of large networks

We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger data sets than those seen elsewhere in the literature. The more flexible framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms are based on variational generalized EM algorithms, where the E-steps are augmented by a minorization-maximization (MM) idea. The bootstrapped standard error estimates are based on an efficient Monte Carlo network simulation idea. Last, we demonstrate the usefulness of the model-based clustering framework by applying it to a discrete-valued network with more than 131,000 nodes and 17 billion edge variables.

Relational event models for social learning in MOOCs

Abstract We propose three extensions for the relational event framework to model the co-evolution of multiple network event streams which are increasingly available thanks to the explosive growth of online applications. Firstly, a flexible stratification approach is considered to allow for more complex data structures with many types of nodes and events. Secondly, an inference method that combines nested case–control sampling with stratification is discussed to scale the approach to very large data sets. Finally, a suite of new temporal and network statistics is introduced to reflect the potentially complex dependencies among events and observed heterogeneities on nodes and edges. The empirical value of new extensions is demonstrated through an analysis of social learning in Massive Open Online Courses (MOOCs). In particular, three modeling problems are considered from the network perspective: (1) the utility of social factors, performance indicators, and clickstream behaviors in the prediction of course dropout, (2) the social and temporal structure of learner interactions across discussion threads, and (3) the forms of mutual dependence of social learning interactions on prior learning success, and future learning success on forms of prior social learning interaction.

Disaster response on September 11, 2001 through the lens of statistical network analysis

The rescue and relief operations triggered by the September 11, 2001 attacks on the World Trade Center in New York City demanded collaboration among hundreds of organisations. To shed light on the response to the September 11, 2001 attacks and help to plan and prepare the response to future disasters, we study the inter-organisational network that emerged in response to the attacks. Studying the inter-organisational network can help to shed light on (1) whether some organisations dominated the inter-organisational network and facilitated communication and coordination of the disaster response; (2) whether the dominating organisations were supposed to coordinate disaster response or emerged as coordinators in the wake of the disaster; and (3) the degree of network redundancy and sensitivity of the inter-organisational network to disturbances following the initial disaster. We introduce a Bayesian framework which can answer the substantive questions of interest while being as simple and parsimonious as possible. The framework allows organisations to have varying propensities to collaborate, while taking covariates into account, and allows to assess whether the inter-organisational network had network redundancy-in the form of transitivity-by using a test which may be regarded as a Bayesian score test. We discuss implications in terms of disaster management.

Statistical modelling of the group structure of social networks

This research evaluates the identification of group structure in social networks through the latent class model and a new Bayesian model comparison method for the number of latent classes. The approach is applied to a well-known network of women in Natchez Mississippi. The latent class analysis reproduces the group structure of the women identified by the original sociologists.

Variational algorithms for biclustering models

Biclustering is an important tool in exploratory statistical analysis which can be used to detect latent row and column groups of different response patterns. However, few studies include covariate data directly into their biclustering models to explain these variations. A novel biclustering framework that considers both stochastic block structures and covariate effects is proposed to address this modeling problem. Fast approximation estimation algorithms are also developed to deal with a large number of latent variables and covariate coefficients. These algorithms are derived from the variational generalized expectation-maximization (EM) framework where the goal is to increase, rather than maximize, the likelihood lower bound in both E and M steps. The utility of the proposed biclustering framework is demonstrated through two block modeling applications in model-based collaborative filtering and microarray analysis.

Relational event models for longitudinal network data with an application to interhospital patient transfers

The main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfers within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available. Copyright