  Node importance estimation problem has been studied conventionally with
homogeneous network topology analysis. To deal with network heterogeneity, a
few recent methods employ graph neural models to automatically learn diverse
sources of information. However, the major concern revolves around that their
full adaptive learning process may lead to insufficient information
exploration, thereby formulating the problem as the isolated node value
prediction with underperformance and less interpretability. In this work, we
propose a novel learning framework: SKES. Different from previous automatic
learning designs, SKES exploits heterogeneous structural knowledge to enrich
the informativeness of node representations. Based on a sufficiently
uninformative reference, SKES estimates the importance value for any input
node, by quantifying its disparity against the reference. This establishes an
interpretable node importance computation paradigm. Furthermore, SKES dives
deep into the understanding that "nodes with similar characteristics are prone
to have similar importance values" whilst guaranteeing that such
informativeness disparity between any different nodes is orderly reflected by
the embedding distance of their associated latent features. Extensive
experiments on three widely-evaluated benchmarks demonstrate the performance
superiority of SKES over several recent competing methods.
