  Graphs consisting of vocal nodes ("the vocal minority") and silent nodes
("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The
vocal nodes tend to have abundant features and labels. In contrast, silent
nodes only have incomplete features and rare labels, e.g., the description and
political tendency of politicians (vocal) are abundant while not for ordinary
people (silent) on the twitter's social network. Predicting the silent majority
remains a crucial yet challenging problem. However, most existing
message-passing based GNNs assume that all nodes belong to the same domain,
without considering the missing features and distribution-shift between
domains, leading to poor ability to deal with VS-Graph. To combat the above
challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN),
which models distribution shifts during message passing and representation
learning by transferring knowledge from vocal nodes to silent nodes.
Specifically, we design the domain-adapted "feature completion and message
passing mechanism" for node representation learning while preserving domain
difference. And a knowledge transferable classifier based on KL-divergence is
followed. Comprehensive experiments on real-world scenarios (i.e., company
financial risk assessment and political elections) demonstrate the superior
performance of our method. Our source code has been open sourced.
