MLN-geeWhiz: A Dashboard for Supporting Complete Life-Cycle of Complex Data Analysis using Multilayer Networks

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Abstract

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 MLNDashboard (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.