  Graph neural networks (GNNs) are widely used in the applications based on
graph structured data, such as node classification and link prediction.
However, GNNs are often used as a black-box tool and rarely get in-depth
investigated regarding whether they fit certain applications that may have
various properties. A recently proposed technique distance encoding (DE) (Li et
al. 2020) magically makes GNNs work well in many applications, including node
classification and link prediction. The theory provided in (Li et al. 2020)
supports DE by proving that DE improves the representation power of GNNs.
However, it is not obvious how the theory assists the applications accordingly.
Here, we revisit GNNs and DE from a more practical point of view. We want to
explain how DE makes GNNs fit for node classification and link prediction.
Specifically, for link prediction, DE can be viewed as a way to establish
correlations between a pair of node representations. For node classification,
the problem becomes more complicated as different classification tasks may hold
node labels that indicate different physical meanings. We focus on the most
widely-considered node classification scenarios and categorize the node labels
into two types, community type and structure type, and then analyze different
mechanisms that GNNs adopt to predict these two types of labels. We also run
extensive experiments to compare eight different configurations of GNNs paired
with DE to predict node labels over eight real-world graphs. The results
demonstrate the uniform effectiveness of DE to predict structure-type labels.
Lastly, we reach three pieces of conclusions on how to use GNNs and DE properly
in tasks of node classification.
