  Network science explores intricate connections among objects, employed in
diverse domains like social interactions, fraud detection, and disease spread.
Visualization of networks facilitates conceptualizing research questions and
forming scientific hypotheses. Networks, as mathematical high-dimensional
objects, require dimensionality reduction for (planar) visualization.
Visualizing empirical networks present additional challenges. They often
contain false positive (spurious) and false negative (missing) edges.
Traditional visualization methods don't account for errors in observation,
potentially biasing interpretations. Moreover, contemporary network data
includes rich nodal attributes. However, traditional methods neglect these
attributes when computing node locations. Our visualization approach aims to
leverage nodal attribute richness to compensate for network data limitations.
We employ a statistical model estimating the probability of edge connections
between nodes based on their covariates. We enhance the Fruchterman-Reingold
algorithm to incorporate estimated dyad connection probabilities, allowing
practitioners to balance reliance on observed versus estimated edges. We
explore optimal smoothing levels, offering a natural way to include relevant
nodal information in layouts. Results demonstrate the effectiveness of our
method in achieving robust network visualization, providing insights for
improved analysis.
