Over the last few decades, simple graphs have been extensively used for studying complex systems of interacting entities from diverse disciplines, such as social networks, transportation, epidemiology, etc. However, when studying data with multiple types of entities, relationships, and features, simple (or even attributed) graphs are not always sufficient. For example, to study accident patterns to take mitigating actions, one needs to explore accident patterns based on factors like weather (rain, sunny, sleet, etc.), light, and road surface conditions in different geographical regions. As another example, to find individuals who are influential across multiple social media, a single graph approach is not well-suited. Indeed, to model such multiple relationships, multiple related graphs are useful. This can be done using multilayer networks (MLNs). Any complex data analysis can immensely benefit from interactive graphic tools rather than working with raw data in command prompt mode. This is especially true as data and models become increasingly complex. To interpret and understand the results of analysis, drill-down, and visualization become critical. The MLN-Dashboard (called MLN-geeWhiz) presented in this demo paper aims to facilitate all aspects of MLN layer generation, analysis, and visualization through an intuitive, interactive web-based dashboard. In this paper, we discuss the dashboard, its architecture, the functionality currently supported, and some use cases.

Covid Wizard or CoWiz is a Covid-19 visualization dashboard based on Multilayer Network (MLN) analysis underneath1. Online dashboards typically plot/visualize statistical information gleaned from raw data, such as daily cases, deaths, recoveries, tests, etc. However, for a better understanding, we need aggregate analysis (e.g., community, centrality) and its visualization which is the purpose of CoWiz. As an example, grouping counties across a country/region based on similarity of increase/decrease in cases, deaths, hospitalizations over intervals is not possible without aggregate analysis. This is where CoWiz utilizes community and other concepts over MLNs that are inferred from Covid and other relevant data sets for visualization.This demo presents a flexible, interactive dashboard which is capable of visualizing various aspects of Covid-19 data, including composition of Covid data with demographics (population density, education level, average earning, vehicle movements, and change in purchase patterns) at the granularity of county for USA. This paper elaborates on the types of analysis, underlying model, and how a flexible visualization dashboard has been developed using open source software and data sets. As new data becomes available, they can be incorporated into the visualization with no manual intervention.