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Visualizations of network and graph data as well as interesting phenomena in graph representation learning.

Visualizing Infectious Disease on Networks

Diffusion across networks is a widespread phenomena across many areas including the spread of viruses in computer networks and the dissemination of rumors across social networks. Here, we explore the spread of infectious disease across social contact networks. First, we consider the simple case in which individuals can only be either susceptible or infected without any long-lasting immunity. This type of infectious disease is similar to the common-cold and influenza . Later, we generalize the infection to more complex settings, considering for example recovered individuals as seen in measles  or asymptomatic spreaders as seen in COVID-19. 

The following network visualizes the simple case on a power law network of 300 nodes where infected individuals are the large nodes and susceptible individuals are the small nodes. Each individual is labelled with a color, indicating its infection pressure at the current timestep computed from the infection statuses of its immediate neighbors. Once an individual exceeds the threshold for infection, it becomes infected and starts spreading to other individuals. For more visualizations of infectious disease settings, for example the inclusion of asymptomatic spreaders, and for varying networks  see the full gallery!

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