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Heterogeneous Information Network (HIN), where nodes and their attributes denote real-world entities and links encode relationships between entities, are ubiquitous in many applications. The presence of multiple types of nodes and links pose significant challenges to the state-of-the-art methods for learning node embeddings from heterogeneous graphs. To address these challenges, we consider three variants of graph variational autoencoder models for heterogeneous networks that avoid the computationally expensive sampling of meta-paths. The proposed methods also maintain uncertainty estimates of node embeddings that help improve generalization performance. We report the results of experiments on link prediction using three different real-world heterogeneous network benchmark data sets that show that the proposed methods significantly outperform state-of-the-art baselines. 

Since the early 2000s, contributors to the deaths-of-despair epidemic such as alcohol and drug related deaths have more than doubled. Evidence of the role of patient networks in the diffusion of prescription behaviors among physicians and of intra-household diffusion of opioids use contribute to important new questions about the population level effects of workplace- and intergenerational networks. This study responds to this need by expanding the focus from households and opioids overdose risk to examine the role of intergenerational and commuting networks in the diffusion of overdose risk from alcohol and drugs more generally among children. Analyses using negative binomial regression combined with computational statistics approaches such as cross-validation and permutation indicated that exposures to extra-local network overdose risks were associated with local adult- and child overdose deaths. These associations remained significant after controlling for multiple socioeconomic and demographic factors. The results showed that the link between network overdose risk and local child overdose deaths was accounted for in large part by intergenerational effects. Above and beyond intergenerational and spatial diffusion, network effects remained significant for Black children. High concentrations of other white residents protected white children against overdose risk but none of the minority groups. In turn, higher concentration of minority residents protected Black and Hispanic children. Higher population density increased the risk of overdose deaths among adults of all racial and ethnic groups, consistent with expectations of social and economic strain. However, it decreased the risk among children, consistent with social control expectations. Implications for future research and policy are discussed. 

This study explored how population mobility flows form commuting networks across US counties and influence the spread of COVID-19. We utilized 3-level mixed effects negative binomial regression models to estimate the impact of network COVID-19 exposure on county confirmed cases and deaths over time. We also conducted weighting-based analyses to estimate the causal effect of network exposure. Results showed that commuting networks matter for COVID-19 deaths and cases, net of spatial proximity, socioeconomic, and demographic factors. Different local racial and ethnic concentrations are also associated with unequal outcomes. These findings suggest that commuting is an important causal mechanism in the spread of COVID-19 and highlight the significance of interconnected of communities. The results suggest that local level mitigation and prevention efforts are more effective when complemented by similar efforts in the network of connected places. Implications for research on inequality in health and flexible work arrangements are discussed. 

Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, i.e., irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kernels, and (ii) They fail to handle multilevel correlation structure in the data. We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR) to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudinal data. Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods. L-DKGPR effectively learns the multilevel correlation with a novel additive kernel that simultaneously accommodates both time-varying and the time-invariant effects. We derive an efficient algorithm to train L-DKGPR using latent space inducing points and variational inference. Results of extensive experiments on several benchmark data sets demonstrate that L-DKGPR significantly outperforms the state-of-the-art longitudinal data analysis (LDA) methods. 

In many real-world applications, e.g., monitoring of individual health, climate, brain activity, environmental exposures, among others, the data of interest change smoothly over a continuum, e.g., time, yielding multi-dimensional functional data. Solving clustering, classification, and regression problems with functional data calls for effective methods for learning compact representations of functional data. Existing methods for representation learning from functional data, e.g., functional principal component analysis, are generally limited to learning linear mappings from the data space to the representation space. However, in many applications, such linear methods do not suffice. Hence, we study the novel problem of learning non-linear representations of functional data. Specifically, we propose functional autoencoders, which generalize neural network autoencoders so as to learn non-linear representations of functional data. We derive from first principles, a functional gradient based algorithm for training functional autoencoders. We present results of experiments which demonstrate that the functional autoencoders outperform the state-of-the-art baseline methods. 

With the increasing adoption of predictive models trained using machine learning across a wide range of high-stakes applications, e.g., health care, security, criminal justice, finance, and education, there is a growing need for effective techniques for explaining such models and their predictions. We aim to address this problem in settings where the predictive model is a black box; That is, we can only observe the response of the model to various inputs, but have no knowledge about the internal structure of the predictive model, its parameters, the objective function, and the algorithm used to optimize the model. We reduce the problem of interpreting a black box predictive model to that of estimating the causal effects of each of the model inputs on the model output, from observations of the model inputs and the corresponding outputs. We estimate the causal effects of model inputs on model output using variants of the Rubin Neyman potential outcomes framework for estimating causal effects from observational data. We show how the resulting causal attribution of responsibility for model output to the different model inputs can be used to interpret the predictive model and to explain its predictions. We present results of experiments that demonstrate the effectiveness of our approach to the interpretation of black box predictive models via causal attribution in the case of deep neural network models trained on one synthetic data set (where the input variables that impact the output variable are known by design) and two real-world data sets: Handwritten digit classification, and Parkinson's disease severity prediction. Because our approach does not require knowledge about the predictive model algorithm and is free of assumptions regarding the black box predictive model except that its input-output responses be observable, it can be applied, in principle, to any black box predictive model.