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New GECC method enables scalable, evolving graph condensation

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Researchers have developed GECC, a novel framework for continual graph condensation designed to handle large-scale and evolving graph data. Unlike previous methods that assume static training sets, GECC allows for efficient updates to a distilled graph without costly retraining, making it suitable for dynamic data streams. The method utilizes class-wise clustering on aggregated features and can incorporate previous condensation results as centroids for expansion, demonstrating superior performance and achieving approximately 1000x speedup on large datasets. AI

RANK_REASON Research paper published on arXiv detailing a new methodology for graph condensation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GECC method enables scalable, evolving graph condensation

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Research paper published on arXiv detailing a new methodology for graph condensation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Scalable Graph Condensation with Evolving Capabilities

    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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