GC-Flow: A Graph-Based Flow Network for Effective Clustering

Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior $p(y|\mathbf{x})$ for semi-supervised classification of graph data. While being effective, as a representation learning approach, the node representations extracted from a GCN often miss useful information for effective clustering, because the objectives are different. In this work, we design normalizing flows that replace GCN layers, leading to a \emph{generative model} that models both the class conditional likelihood $p(\mathbf{x}|y)$ and the class prior $p(y)$. The resulting neural network, GC-Flow, retains the graph convolution operations while being equipped with a Gaussian mixture representation space. It enjoys two benefits: it not only maintains the predictive power of GCN, but also produces well-separated clusters, due to the structuring of the representation space. We demonstrate these benefits on a variety of benchmark data sets. Moreover, we show that additional parameterization, such as that on the adjacency matrix used for graph convolutions, yields additional improvement in clustering.Comment: ICML 2023. Code is available at https://github.com/xztcwang/GCFlo

A review of methods to estimate the visibility factor for bias correction in network scale-up studies

Network scale-up is an indirect size estimation method, in which participants are questioned on sensitive behaviors of their social network members. Therefore, the visibility of the behavior affects the replies and estimates. Many attempts to estimate visibility have been made. The aims of this study were to review the main methods used to address visibility and to provide a summary of reported visibility factors (VFs) across populations. We systematically searched relevant databases and Google. In total, 15 studies and reports that calculated VFs were found. VF calculation studies have been applied in 9 countries, mostly in East Asia and Eastern Europe. The methods applied were expert opinion, comparison of NSU with another method, the game of contacts, social respect, and the coming-out rate. The VF has been calculated for heavy drug users, people who inject drugs (PWID), female sex workers (FSWs) and their clients, male who have sex with male (MSM), alcohol and methamphetamine users, and those who have experienced extra-/pre-marital sex and abortion. The VF varied from 1.4% in Japan to 52.0% in China for MSM; from 34.0% in Ukraine to 111.0% in China for FSWs; and from 12.0% among Iranian students to 57.0% in Ukraine for PWID. Our review revealed that VF estimates were heterogeneous, and were not available for most settings, in particular the Middle East and North Africa region, except Iran. More concrete methodologies to estimate the VF are required

Regional HIV knowledge hubs: a new approach by the health sector to transform knowledge into practice

This study aims to introduce the knowledge hub (KH) as an initiative to facilitate transformation of knowledge into practice and to highlight the activity and limitations with this new policy. The study was conducted through a review of articles; expert views in this field were sought for further information. Regional human immunodeficiency virus (HIV) KHs were developed by the World Health Organization and GTZ. A series of activities including capacity building, development of training models, technical assistance, and application of studies are provided through these hubs. However, financial limitations are the main obstacle in achieving these aims. This piece of work introduces these HIV hubs in order to help countries, particularly developing countries, provide the support needed to fight the progression of HIV

A New Learning Algorithm for the MAXQ Hierarchical Reinforcement Learning Method

One of the most effective methods in hierarchical reinforcement learning is MAXQ method introduced in [1]. Although this method is shown to be effective in many applications, it is computationally expensive in applications with deep hierarchy [2], which makes it impractical for use in such applications. In this paper, we propose a new learning algorithm for MAXQ method to address the open problem of reducing its computational complexity. This new algorithm, which is an improved version of MAXQ-Q learning algorithm [2], learns value functions instead of computing them with a complete search of all paths thorough the MAXQ graph. We use the new learning algorithm to solve some instances of the simple Taxi Domain Problem. In this domain, our experimental results show that the new learning algorithm always converges to optimal policy, its convergence behavior is similar to MAXQ-Q learning algorithm, and as it is expected, its overall running time is less than MAXQ-Q learning algorithm

Convex Co-embedding

We present a general framework for association learning, where entities are embedded in a common latent space to express relatedness by geometry -- an approach that underlies the state of the art for link prediction, relation learning, multi-label tagging, relevance retrieval and ranking. Although current approaches rely on local training applied to non-convex formulations, we demonstrate how general convex formulations can be achieved for entity embedding, both for standard multi-linear and prototype-distance models. We investigate an efficient optimization strategy that allows scaling. An experimental evaluation reveals the advantages of global training in different case studies

Application of particle swarm optimization approach in the inflationary inventory model under stochastic conditions

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