  This paper investigates a missing feature imputation problem for graph
learning tasks. Several methods have previously addressed learning tasks on
graphs with missing features. However, in cases of high rates of missing
features, they were unable to avoid significant performance degradation. To
overcome this limitation, we introduce a novel concept of channel-wise
confidence in a node feature, which is assigned to each imputed channel feature
of a node for reflecting certainty of the imputation. We then design
pseudo-confidence using the channel-wise shortest path distance between a
missing-feature node and its nearest known-feature node to replace unavailable
true confidence in an actual learning process. Based on the pseudo-confidence,
we propose a novel feature imputation scheme that performs channel-wise
inter-node diffusion and node-wise inter-channel propagation. The scheme can
endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves
state-of-the-art accuracy for both semi-supervised node classification and link
prediction on various datasets containing a high rate of missing features.
Codes are available at https://github.com/daehoum1/pcfi.
