In this paper, we propose an innovative solution by constructing infinite tree-structured PGMs that correspond exactly to neural networks. Our research reveals that DNNs, during forward propagation, indeed perform approximations of PGM inference that are precise in this alternative PGM structure. Not only does our research complement existing studies that describe neural networks as kernel machines or infinite-sized Gaussian processes, it also elucidates a more direct approximation that DNNs make to exact inference in PGMs. Potential benefits include improved pedagogy and interpretation of DNNs, and algorithms that can merge the strengths of PGMs and DNNs.

In this paper, we consider data consisting of multiple networks, each comprised of a different edge set on a common set of nodes. Many models have been proposed for such multi-view data, assuming that the data views are closely related. In this paper, we provide tools for evaluating the assumption that there is a relationship between the different views. In particular, we ask: is there an association between the latent community memberships of the nodes within each data view? To answer this question, we extend the stochastic block model for a single network view to two network views, and develop a new hypothesis test for the null hypothesis that the latent community structure within each data view is independent. We apply our test to protein-protein interaction data sets from the HINT database (Das & Yu 2012). We find evidence of a weak association between the latent community structure of proteins defined with respect to binary interaction data and with respect to co-complex association data. We also extend this proposal to the setting of a network with node covariates.

Multi-view networks are ubiquitous in real-world applications. In order to extract knowledge or business value, it is of interest to transform such networks into representations that are easily machine-actionable. Meanwhile, network embedding has emerged as an effective approach to generate distributed network representations. Therefore, we are motivated to study the problem of multi-view network embedding, with a focus on the characteristics that are specific and important in embedding this type of networks. In our practice of embedding real-world multi-view networks, we identify two such characteristics, which we refer to as preservation and collaboration. We then explore the feasibility of achieving better embedding quality by simultaneously modeling preservation and collaboration, and propose the mvn2vec algorithms. With experiments on a series of synthetic datasets, an internal Snapchat dataset, and two public datasets, we further confirm the presence and importance of preservation and collaboration. These experiments also demonstrate that better embedding can be obtained by simultaneously modeling the two characteristics, while not over-complicating the model or requiring additional supervision.

Bayesian networks are a theoretically well-founded approach to represent large multi-variate probability distributions, and have proven useful in a broad range of applications. While several software tools for visualizing and editing Bayesian networks exist, they have important weaknesses when it comes to enabling users to clearly understand and compare conditional probability tables in the context of network topology, especially in large-scale networks. This paper describes a system for improving the ability for computers to work with people to develop intelligent systems through the construction of high-performing Bayesian networks. We describe NetEx, a tool developed as a Cytoscape plug-in, which allows a user to visually inspect and compare details concerning multiple nodes in a Bayesian network while maintaining awareness of their network context. It uses a "thought bubble line" to connect nodes in a graph representation and their internal information at the side of the graph. The tool seeks to improve the ability of experts to analyze and debug large Bayesian network models, and to help people to understand how alternative algorithms and Bayesian networks operate, providing insights into how to improve them.

Humans are confronted with an increasingly complex array of ingestion substances and dietary choices that influence health and well being. However, even with strong medical evidence that clearly links ingestion strategies and heath consequences, the general public struggles to make health-optimizing ingestion decisions. Based on our literature review, we delineate a typology of barriers to formulating health-optimizing ingestion strategies. We propose that the introduction of artificial intelligence (AI) as “decision management” (AI-DM) technology into the ingestion decision-making network would increase the likelihood of more predictable and optimized health outcomes. Also, we delineate the key informational constituencies needed to enable a comprehensive and effective AI-DM system. While no author has yet proposed AI in the particular context discussed in this paper, the theoretical and empirical literature suggests that this might be possible. We conclude by discussing areas for additional research.