GC-Bench: An Open and Unified Benchmark for Graph Condensation
<|reference_start|>GC-Bench: An Open and Unified Benchmark for Graph Condensation: Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core concept of GC is to create a smaller, more manageable graph that retains the characteristics of the original graph. Despite the proliferation of graph condensation methods developed in recent years, there is no comprehensive evaluation and in-depth analysis, which creates a great obstacle to understanding the progress in this field. To fill this gap, we develop a comprehensive Graph Condensation Benchmark (GC-Bench) to analyze the performance of graph condensation in different scenarios systematically. Specifically, GC-Bench systematically investigates the characteristics of graph condensation in terms of the following dimensions: effectiveness, transferability, and complexity. We comprehensively evaluate 12 state-of-the-art graph condensation algorithms in node-level and graph-level tasks and analyze their performance in 12 diverse graph datasets. Further, we have developed an easy-to-use library for training and evaluating different GC methods to facilitate reproducible research. The GC-Bench library is available at https://github.com/RingBDStack/GC-Bench.<|reference_end|>
@article{sun2024gc-bench:,
    title={GC-Bench: An Open and Unified Benchmark for Graph Condensation},
    author={Qingyun Sun, Ziying Chen, Beining Yang, Cheng Ji, Xingcheng Fu, Sheng
  Zhou, Hao Peng, Jianxin Li, Philip S. Yu},
    journal={arXiv preprint arXiv:2407.00615},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00615},
    primaryClass={cs.LG}
}