Simple Graph Condensation

The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph. Existing methods primarily focus on aligning key metrics between the condensed and original graphs, such as gradients, distribution and trajectory of GNNs, yielding satisfactory performance on downstream tasks. However, these complex metrics necessitate intricate computations and can potentially disrupt the optimization process of the condensation graph, making the condensation process highly demanding and unstable. Motivated by the recent success of simplified models in various fields, we propose a simplified approach to metric alignment in graph condensation, aiming to reduce unnecessary complexity inherited from GNNs. In our approach, we eliminate external parameters and exclusively retain the target condensed graph during the condensation process. Following the hierarchical aggregation principles of GNNs, we introduce the Simple Graph Condensation (SimGC) framework, which aligns the condensed graph with the original graph from the input layer to the prediction layer, guided by a pre-trained Simple Graph Convolution (SGC) model on the original graph. As a result, both graphs possess the similar capability to train GNNs. This straightforward yet effective strategy achieves a significant speedup of up to 10 times compared to existing graph condensation methods while performing on par with state-of-the-art baselines. Comprehensive experiments conducted on seven benchmark datasets demonstrate the effectiveness of SimGC in prediction accuracy, condensation time, and generalization capability. Our code will be made publicly available.

Disentangled Condensation for Large-scale Graphs

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 https://github.com/BangHonor/DisCo.