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 this https URL

我们研究了图缩减的性能，并通过开发一个综合的图缩减基准测试库 (GC-Bench) 来评估 12 种最新的图缩减算法在不同场景下的效果、可迁移性和复杂性。

GC-Bench: 图形凝聚的开放和统一基准

GC-Bench: An Open and Unified Benchmark for Graph Condensation

Graph condensation (GC) is an emerging technique designed to learn a
significantly smaller graph that retains the essential information of the
original graph. This condensed graph has shown promise in accelerating graph
neural networks while preserving performance comparable to those achieved with
the original, larger graphs. Additionally, this technique facilitates
downstream applications such as neural architecture search and enhances our
understanding of redundancy in large graphs. Despite the rapid development of
GC methods, a systematic evaluation framework remains absent, which is
necessary to clarify the critical designs for particular evaluative aspects.
Furthermore, several meaningful questions have not been investigated, such as
whether GC inherently preserves certain graph properties and offers robustness
even without targeted design efforts. In this paper, we introduce GC-Bench, a
comprehensive framework to evaluate recent GC methods across multiple
dimensions and to generate new insights. Our experimental findings provide a
deeper insights into the GC process and the characteristics of condensed
graphs, guiding future efforts in enhancing performance and exploring new
applications. Our code is available at
https://github.com/Emory-Melody/GraphSlim/tree/main/benchmark.

图压缩是一种新兴技术，通过学习一个显著较小但保留原图关键信息的压缩图，加速图神经网络并保持性能，在神经架构搜索等下游应用和对大规模图冗余性的理解方面具有潜力。本文介绍 GC-Bench，一个综合评估图压缩方法的框架，提供对 GC 过程和压缩图特性的深入洞察，指导未来的性能增强和探索新应用。