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English(EN) Scalable Graph Condensation with Evolving Capabilities

新的GECC方法实现了可扩展、不断发展的图压缩

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了GECC，一种新颖的持续图压缩框架，旨在处理大规模和不断发展的图数据。与假设静态训练集的先前方法不同，GECC允许在不进行昂贵重新训练的情况下高效更新蒸馏图，使其适用于动态数据流。该方法利用聚合特征上的类簇聚类，并将先前的压缩结果作为质心进行扩展，表现出卓越的性能，在大数据集上实现了约1000倍的加速。 AI

排序理由 研究论文发表在arXiv上，详细介绍了图压缩的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的GECC方法实现了可扩展、不断发展的图压缩

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研究论文发表在arXiv上，详细介绍了图压缩的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shengbo Gong, Mohammad Hashemi, Juntong Ni, Carl Yang, Wei Jin ·

    可扩展图压缩与演进能力

    arXiv:2502.17614v3 Announce Type: replace Abstract: The rapid growth of graph data creates significant scalability challenges as most graph algorithms scale quadratically with size. To mitigate these issues, Graph Condensation (GC) methods have been proposed to learn a small grap…

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