  Graph neural networks (GNNs) encounter significant computational challenges
when handling large-scale graphs, which severely restricts their efficacy
across diverse applications. To address this limitation, graph condensation has
emerged as a promising technique, which constructs a small synthetic graph for
efficiently training GNNs while retaining performance. However, due to the
topology structure among nodes, graph condensation is limited to condensing
only the observed training nodes and their corresponding structure, thus
lacking the ability to effectively handle the unseen data. Consequently, the
original large graph is still required in the inference stage to perform
message passing to inductive nodes, resulting in substantial computational
demands. To overcome this issue, we propose mapping-aware graph condensation
(MCond), explicitly learning the one-to-many node mapping from original nodes
to synthetic nodes to seamlessly integrate new nodes into the synthetic graph
for inductive representation learning. This enables direct information
propagation on the synthetic graph, which is much more efficient than on the
original large graph. Specifically, MCond employs an alternating optimization
scheme with innovative loss terms from transductive and inductive perspectives,
facilitating the mutual promotion between graph condensation and node mapping
learning. Extensive experiments demonstrate the efficacy of our approach in
inductive inference. On the Reddit dataset, MCond achieves up to 121.5x
inference speedup and 55.9x reduction in storage requirements compared with
counterparts based on the original graph.
