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Geographic network visualizations often require assigning nodes to geographic coordinates, but this can be challenging when precise node locations are undefined. We explore this problem using U.S. senators as a case study. Each state has two senators, and thus it is difficult to assign clear individual locations. We devise eight different node placement strategies ranging from geometric approaches such as state centroids and longest axis midpoints to data-driven methods using population centers and home office locations. Through expert evaluation, we found that specific coordinates such as senators’ office locations and state centroids are preferred strategies, while random placements and the longest axis method are least favored. The findings also highlight the importance of aligning node placement with research goals and avoiding potentially misleading encodings. This paper contributes to future advancements in geospatial network visualization software development and aims to facilitate more effective exploratory spatial data analysis. 

A common theme among previously proposed models for network epidemics is the assumption that the propagating object (e.g., a pathogen [in the context of infectious disease propagation] or a piece of information [in the context of information propagation]) is transferred across network nodes without going through any modification or evolutionary adaptations. However, in real-life spreading processes, pathogens often evolve in response to changing environments and medical interventions, and information is often modified by individuals before being forwarded. In this article, we investigate the effects of evolutionary adaptations on spreading processes in complex networks with the aim of 1) revealing the role of evolutionary adaptations on the threshold, probability, and final size of epidemics and 2) exploring the interplay between the structural properties of the network and the evolutionary adaptations of the spreading process.