Graph condensation has emerged as an intriguing technique to provide Graph
Neural Networks for large-scale graphs with a more compact yet informative
small graph to save the expensive costs of large-scale graph learning. Despite
the promising results achieved, previous graph condensation methods often
employ an entangled condensation strategy that involves condensing nodes and
edges simultaneously, leading to substantial GPU memory demands. This entangled
strategy has considerably impeded the scalability of graph condensation,
impairing its capability to condense extremely large-scale graphs and produce
condensed graphs with high fidelity. Therefore, this paper presents
Disentangled Condensation for large-scale graphs, abbreviated as DisCo, to
provide scalable graph condensation for graphs of varying sizes. At the heart
of DisCo are two complementary components, namely node and edge condensation
modules, that realize the condensation of nodes and edges in a disentangled
manner. In the node condensation module, we focus on synthesizing condensed
nodes that exhibit a similar node feature distribution to original nodes using
a pre-trained node classification model while incorporating class centroid
alignment and anchor attachment regularizers. After node condensation, in the
edge condensation module, we preserve the topology structure by transferring
the link prediction model of the original graph to the condensed nodes,
generating the corresponding condensed edges. Based on the disentangled
strategy, the proposed DisCo can successfully scale up to the ogbn-papers100M
graph with over 100 million nodes and 1 billion edges with flexible reduction
rates. Extensive experiments on five common datasets further demonstrate that
the proposed DisCo yields results superior to state-of-the-art counterparts by
a significant margin. The source code is available at
this https URL

本论文提出了用于各种大小的大规模图的可扩展图压缩方法 DisCo，其中有两个互补的组件，即节点压缩模块和边压缩模块，实现了节点和边的分离压缩。通过在预训练节点分类模型中合并类别质心对齐和锚点附加正则化器，节点压缩模块可以生成类似于原始节点的特征分布的合成节点，并将原始图的连边预测模型转移到压缩的节点中，生成相应的压缩边。实验证明，DisCo 方法在五个常见数据集上的结果明显优于目前最先进的方法。