Improving Robustness of Scale-Free Networks to Message Distortion

Vast numbers of organizations and individuals communicate every day by sending messages over social networks. These messages, however, are subject to change as they propagate through the network. This paper attempts to calculate the distortion of a message as it propagates in a social network with a scale free topology, and to establish a remedial process in which a node will correct the distortion during the diffusion process, in order to improve the robustness of scale-free networks to message distortion. We test a model that we created using a simulation of different types of scale-free networks, and we compared different sets of corrective nodes, hubs, regular (non-hubs) nodes, and a combination of hubs and regular nodes. The findings show that using hubs that correct the distorted message while it\u27s diffused, decrease a global error measurement of the distortion, and as a result improve the robustness of the network

Analyzing Non-Alcoholic Fatty Liver Disease Risk Using Time-Series Model

Non-alcoholic fatty liver disease (NAFLD) is the most global frequent liver disease, with a prevalence of almost 20% in the overall population. NAFLD may progress to fibrosis and later into cirrhosis in addition to other diseases. Our objective is to stratify patients\u27 risks for NAFLD and advanced fibrosis over time and suggest preventive medical decisions. We used a cohort of individuals from the Tel-Aviv medical center. Time-series clustering machine learning model (Hidden Markov Models (HMM)) was used to profile fibrosis risk by modeling patients’ latent medical status and trajectories over time. The best-fitting model had three latent HMM states. Initial results show that tracking individuals over time and their relative risk for fibrosis at each point of time provides significant clinical insights regarding each state (and its group of individuals). Thus, longitudinal risk stratification can enable the early identification of specific individual groups following distinct medical trajectories based on their routine visits