  With the development of various applications, such as social networks and
knowledge graphs, graph data has been ubiquitous in the real world.
Unfortunately, graphs usually suffer from being absent due to
privacy-protecting policies or copyright restrictions during data collection.
The absence of graph data can be roughly categorized into attribute-incomplete
and attribute-missing circumstances. Specifically, attribute-incomplete
indicates that a part of the attribute vectors of all nodes are incomplete,
while attribute-missing indicates that the whole attribute vectors of partial
nodes are missing. Although many efforts have been devoted, none of them is
custom-designed for a common situation where both types of graph data absence
exist simultaneously. To fill this gap, we develop a novel network termed
Revisiting Initializing Then Refining (RITR), where we complete both
attribute-incomplete and attribute-missing samples under the guidance of a
novel initializing-then-refining imputation criterion. Specifically, to
complete attribute-incomplete samples, we first initialize the incomplete
attributes using Gaussian noise before network learning, and then introduce a
structure-attribute consistency constraint to refine incomplete values by
approximating a structure-attribute correlation matrix to a high-order
structural matrix. To complete attribute-missing samples, we first adopt
structure embeddings of attribute-missing samples as the embedding
initialization, and then refine these initial values by adaptively aggregating
the reliable information of attribute-incomplete samples according to a dynamic
affinity structure. To the best of our knowledge, this newly designed method is
the first unsupervised framework dedicated to handling hybrid-absent graphs.
Extensive experiments on four datasets have verified that our methods
consistently outperform existing state-of-the-art competitors.
