Graph condensation aims to reduce the size of a large-scale graph dataset by
synthesizing a compact counterpart without sacrificing the performance of Graph
Neural Networks (GNNs) trained on it, which has shed light on reducing the
computational cost for training GNNs. Nevertheless, existing methods often fall
short of accurately replicating the original graph for certain datasets,
thereby failing to achieve the objective of lossless condensation. To
understand this phenomenon, we investigate the potential reasons and reveal
that the previous state-of-the-art trajectory matching method provides biased
and restricted supervision signals from the original graph when optimizing the
condensed one. This significantly limits both the scale and efficacy of the
condensed graph. In this paper, we make the first attempt toward
\textit{lossless graph condensation} by bridging the previously neglected
supervision signals. Specifically, we employ a curriculum learning strategy to
train expert trajectories with more diverse supervision signals from the
original graph, and then effectively transfer the information into the
condensed graph with expanding window matching. Moreover, we design a loss
function to further extract knowledge from the expert trajectories. Theoretical
analysis justifies the design of our method and extensive experiments verify
its superiority across different datasets. Code is released at
this https URL

通过使用课程学习策略和扩展窗口匹配技术，本文尝试进行无损图稀疏化，并设计了一种损失函数来从专家轨迹中提取知识，理论分析证明了方法的合理性，并通过广泛实验证实其优越性。