  Disruptive events significantly alter spatial and social interactions among
people and places. To examine the structural changes in spatial and social
interaction networks in pre- and post-periods of the COVID-19 pandemic, we
employ the Louvain method to algorithmically detect regions (communities)
within the county-to-county networks of the SafeGraph mobility and Facebook
social connectedness. We then utilize a range of partition similarity metrics,
including adjusted Rand, z-Rand, Normalized Mutual Information (NMI), and
Jaccard indices, to quantitatively measure the similarity of regions between
the pre- and post-periods partitions of each network. Our findings reveal that
in the post-pandemic period, spatial interactions led to the formation of
localized geographic communities or regions characterized by higher modular
activity within each region. In contrast, online social interactions shifted
towards longer distance connections, resulting in the emergence of larger
regions marked by strong friendship ties that often encompassed multiple
states. By understanding these changes, we contribute to a better comprehension
of the pandemic's impact on our interconnected physical-virtual world,
providing valuable insights for future research and informing strategies to
adapt to the evolving dynamics of human interactions.
