Abstract: Highlights•In the real world, it is difficult to analyze attribute-missing graphs.•Propose a novel self-supervised graph representation learning method for attribute-missing graphs.•Perform attribute imputation in the input space of the attribute-missing graph.•Demonstrate state-of-the-art performance on four benchmark datasets.
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