![[서울대 AI 여름학교] CMU 유재민 - Accurate Node Feature Estimation with ...](https://i.ytimg.com/vi/ODIlNr59FCE/maxresdefault.jpg)
%2C%20discrete%20(int4%20type)%2C%20and%20interpretable%20node%20representations%2C%20termed%20node%20identifiers%20(node%20IDs)%2C%20to%20tackle%20inference%20challenges%20on%20large-scale%20graphs.%20By%20employing%20vector%20quantization%2C%20we%20compress%20continuous%20node%20embeddings%20from%20multiple%20layers%20of%20a%20Graph%20Neural%20Network%20(GNN)%20into%20discrete%20codes%2C%20applicable%20under%20both%20self-supervised%20and%20supervised%20learning%20paradigms.%20These%20node%20IDs%20capture%20high-level%20abstractions%20of%20graph%20data%20and%20offer%20interpretability%20that%20traditional%20GNN%20embeddings%20lack.%20Extensive%20experiments%20on%2034%20datasets%2C%20encompassing%20node%20classification%2C%20graph%20classification%2C%20link%20prediction%2C%20and%20attributed%20graph%20clustering%20tasks%2C%20demonstrate%20that%20the%20generated%20node%20IDs%20significantly%20enhance%20speed%20and%20memory%20efficiency%20while%20achieving%20competitive%20performance%20compared%20to%20current%20state-of-the-art%20methods.)
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