  Graph-structured data and their related algorithms have attracted significant
attention in many fields, such as influenza prediction in public health.
However, the variable influenza seasonality, occasional pandemics, and domain
knowledge pose great challenges to construct an appropriate graph, which could
impair the strength of the current popular graph-based algorithms to perform
data analysis. In this study, we develop a novel method, Dynamic Virtual Graph
Significance Networks (DVGSN), which can supervisedly and dynamically learn
from similar "infection situations" in historical timepoints. Representation
learning on the dynamic virtual graph can tackle the varied seasonality and
pandemics, and therefore improve the performance. The extensive experiments on
real-world influenza data demonstrate that DVGSN significantly outperforms the
current state-of-the-art methods. To the best of our knowledge, this is the
first attempt to supervisedly learn a dynamic virtual graph for time-series
prediction tasks. Moreover, the proposed method needs less domain knowledge to
build a graph in advance and has rich interpretability, which makes the method
more acceptable in the fields of public health, life sciences, and so on.
