  With the increasing demands of training graph neural networks (GNNs) on
large-scale graphs, graph data condensation has emerged as a critical technique
to relieve the storage and time costs during the training phase. It aims to
condense the original large-scale graph to a much smaller synthetic graph while
preserving the essential information necessary for efficiently training a
downstream GNN. However, existing methods concentrate either on optimizing node
features exclusively or endeavor to independently learn node features and the
graph structure generator. They could not explicitly leverage the information
of the original graph structure and failed to construct an interpretable graph
structure for the synthetic dataset. To address these issues, we introduce a
novel framework named \textbf{G}raph Data \textbf{C}ondensation via
\textbf{S}elf-expressive Graph Structure \textbf{R}econstruction
(\textbf{GCSR}). Our method stands out by (1) explicitly incorporating the
original graph structure into the condensing process and (2) capturing the
nuanced interdependencies between the condensed nodes by reconstructing an
interpretable self-expressive graph structure. Extensive experiments and
comprehensive analysis validate the efficacy of the proposed method across
diverse GNN models and datasets. Our code is available at
\url{https://github.com/zclzcl0223/GCSR}.
