Learning of Weighted Dynamic Multi-layer Networks via Latent Gaussian Processes

International audienc

A multilayered block network model to forecast large dynamic transportation graphs:An application to US air transport

Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art latent network model to forecast multilayer dynamic graphs that are increasingly common in transportation and proposes a community-based extension to reduce the computational burden. Flexible time series analysis is obtained by modeling the probability of edges between vertices through latent Gaussian processes. The models and Bayesian inference are illustrated on a sample of 10-year data from four major airlines within the US air transportation system. Results show how the estimated latent parameters from the models are related to the airline's connectivity dynamics, and their ability to project the multilayer graph into the future for out-of-sample full network forecasts, while stochastic blockmodeling allows for the identification of relevant communities. Reliable network predictions would allow policy-makers to better understand the dynamics of the transport system, and help in their planning on e.g. route development, or the deployment of new regulations

Brain network mechanisms in learning behavior

The study of learning has been a central focus of psychology and neuroscience since their inception. Cognitive neuroscience’s traditional approach to understanding learn-ing has been to decompose it into discrete cognitive processes with separable and localized underlying neural systems. While this focus on modular cognitive functions for individual brain areas has led to considerable progress, there is increasing evidence that much of learn-ing behavior relies on overlapping cognitive and neural systems, which may be harder to disentangle than previously envisioned. This is not surprising, as the processes underlying learning must involve widespread integration of information from sensory, affective, and motor sources. The standard tools of cognitive neuroscience limit our ability to describe processes that rely on widespread coordination of brain activity. To understand learning, it will be necessary to characterize dynamic co-activation at the circuit level. In this dissertation, I present three studies that seek to describe the roles of distrib-uted brain networks in learning. I begin by giving an overview of our current understand-ing of multiple forms of learning, describing the neural and computational mechanisms thought to underlie incremental feedback-based learning and flexible episodic memory. I will focus in particular on the difficulties in separating these processes at the cognitive level and in localizing them to individual regions at the neural level. I will then describe recent findings that have begun to characterize the brain’s large-scale network structure, emphasiz-ing the potential roles that distributed networks could play in understanding learning and cognition more generally. I will end the introduction by reviewing current attempts to char-acterize the dynamics of large-scale brain networks, which will be essential for providing a mechanistic link to learning behavior. Chapter 2 is a study demonstrating that intrinsic connectivity between the hippo-campus and the ventromedial prefrontal cortex, as well as between these regions and dis-tributed brain networks, is related to individual differences in the transfer of learning on a sensory preconditioning task. The hippocampus and ventromedial prefrontal cortex have both been shown to be involved in this type of learning, and this study represents an early attempt to link connectivity between individual regions and broader networks to learning processes. Chapter 3 is a study that takes advantage of recent developments in mathematical modeling of temporal networks to demonstrate a relationship between large-scale network dynamics and reinforcement learning within individuals. This study shows that the flexibil-ity of network connectivity in the striatum is related to learning performance over time, as well as to individual differences in parameters estimated from computational models of re-inforcement learning. Notably, connectivity between the striatum and visual as well as or-bitofrontal regions increased over the course of the task, which is consistent with an inte-grative role for the region in learning value-based associations. Network flexibility in a dis-tinct set of regions is associated with episodic memory for object images presented during the learning task. Chapter 4 examines the role of dopamine, a neurotransmitter strongly linked to val-ue updating in reinforcement learning, in the dynamic network changes occurring during learning. Patients with Parkinson’s disease, who experience a loss of dopaminergic neu-rons in the substantia nigra, performed a reversal-learning task while undergoing functional magnetic resonance imaging. Patients were scanned on and off of a dopamine precursor medication (levodopa) in a within-subject design in order to examine the impact of dopa-mine on brain network dynamics during learning. The reversal provided an experimental manipulation of dynamic connectivity, and patients on medication showed greater modula-tion of striatal-cortical connectivity. Similar results were found in a number of regions re-ceiving midbrain projections including the prefrontal cortex and medial temporal lobe. This study indicates that dopamine inputs from the midbrain modulate large-scale network dy-namics during learning, providing a direct link between reinforcement learning theories of value updating and network neuroscience accounts of dynamic connectivity. Together, these results indicate that large-scale networks play a critical role in multi-ple forms of learning behavior. Each highlights the potential importance of understanding dynamic routing and integration of information across large-scale circuits for our concep-tion of learning and other cognitive processes. Understanding the when, where, and how of this information flow in the brain may provide an alternative or compliment to traditional theories of distinct learning systems. These studies also illustrate challenges in integrating this perspective with established theories in cognitive neuroscience. Chapter 5 will situate the studies in a broader discussion of how brain activity relates to cognition in general, while pointing out current roadblocks and potential ways forward for a cognitive network neuroscience of learning

The Science of Human Connection: A Study of the Effect of Social Networks on Acute Gastrointestinal Illness in Rural Ecuadorian Communities

Background Diarrheal disease is an important cause of childhood mortality and is spread by two main mechanisms: human contact and contamination of the environment. Though individual- and household-level Water, Sanitation, and Hygiene (WASH) interventions are primarily used to intersect these transmission pathways, seldom are community-level factors considered to ensure both intervention adoption and sustainability. Social constructs like social cohesion are believed to influence the quality and effectiveness of interventions, especially those based on action at the community-level. Few studies, however, identify a causal framework for how social constructs impact WASH interventions and diarrheal disease occurrence, and fewer use social network data. Previous studies in coastal Ecuador showed diarrheal disease spreads more slowly to and in rural villages that have a greater density of social ties, suggesting a greater spread of individual and collective water practices that help reduce transmission of diarrheal disease. Objective This dissertation research aims to extend previous work by methodically defining social cohesion as an important social construct using different types of social network data, examining temporal variability of the effect of social cohesion on diarrheal disease, whether this relationship is mediated by WASH, and the role that gender plays in social cohesion and WASH in rural, coastal Ecuador. Methods Using longitudinal sociometric data from villages in rural, coastal Ecuador, we identify important network determinants of social cohesion and in turn the temporal effect of social cohesion on WASH interventions and diarrheal disease incidence. We use statistics for the analysis of network graph data and a novel two-stage Bayesian hierarchical model. We importantly theorize a causal framework for the observed phenomena through use of qualitative methods. Results Different types of social networks illustrate the multidimensionality of social processes at the household- and community-levels that influence diarrheal disease incidence. While a network comprised of individuals who pass time together becomes a stronger measure of risk over time, due to density of people and increased travel, having a network of core discussants with whom to discuss important matters is a consistent measure of protection. Having a strong community network of core discussants results in 0.87 (0.71, 1.06) fewer odds of diarrheal disease in 2007 and 0.34 (0.26, 0.45) fewer odds of diarrheal disease by 2013. This protective effect is partially mediated by WASH related factors like community sanitation and improved water use over time, suggesting the importance of social constructs at the community-level for intervention implementation and in turn the reduction of diarrheal disease. Qualitative data collected in the same communities, however, revealed the contributions of infrastructural development and an increasing wage economy to the increasing importance of community. Qualitative data also revealed the importance of gender equity for both community social cohesion and adoption of WASH practices. Analysis of social network data shows communities that are more assortative by gender (i.e. that have less gender equity) are less likely to engage in WASH practices at the household-level over time. Significance By understanding how community correlates of social networks affect intervention practices and diarrheal disease transmission, we can leverage social networks to influence positive behavior change and WASH infrastructure. This research objective is in line with target 5 and 6b of the United Nations Sustainable Development Goals, which aim to achieve gender equality and support and strengthen participation of local communities in improving WASH.PHDEpidemiological ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147711/1/hegdes_1.pd

Bayesian learning of dynamic multilayer networks

A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges. In this paper, we focus on the time-varying interconnections among a set of actors in multiple contexts, called layers. Current literature lacks flexible statistical models for dynamic multilayer networks, which can enhance quality in inference and prediction by efficiently borrowing information within each network, across time, and between layers. Motivated by this gap, we develop a Bayesian nonparametric model leveraging latent space representations. Our formulation characterizes the edge probabilities as a function of shared and layer-specific actors positions in a latent space, with these positions changing in time via Gaussian processes. This representation facilitates dimensionality reduction and incorporates different sources of information in the observed data. In addition, we obtain tractable procedures for posterior computation, inference, and prediction. We provide theoretical results on the flexibility of our model. Our methods are tested on simulations and infection studies monitoring dynamic face-to-face contacts among individuals in multiple days, where we perform better than current methods in inference and prediction

Bayesian learning of dynamic multilayer networks

A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges. In this paper, we focus on the time-varying interconnections among a set of actors in multiple contexts, called layers. Current literature lacks flexible statistical models for dynamic multilayer networks, which can enhance quality in inference and prediction by efficiently borrowing information within each network, across time, and between layers. Motivated by this gap, we develop a Bayesian nonparametric model leveraging latent space representations. Our formulation characterizes the edge probabilities as a function of shared and layer-specific actors positions in a latent space, with these positions changing in time via Gaussian processes. This representation facilitates dimensionality reduction and incorporates different sources of information in the observed data. In addition, we obtain tractable procedures for posterior computation, inference, and prediction. We provide theoretical results on the flexibility of our model. Our methods are tested on simulations and infection studies monitoring dynamic face-to-face contacts among individuals in multiple days, where we perform better than current methods in inference and prediction