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English(EN) Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

新的SPGCL方法通过优化正样本增强图对比学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为SPGCL的新方法来改进图对比学习（GCL）。他们发现，现有的GCL方法由于图编码器中的消息传递机制，常常无法有效地从正样本中学习。SPGCL旨在通过选择性地传播高能量特征并使用低能量特征进行更可靠的正样本采样来解决这个问题，从而在实验中获得更好的性能。 AI

影响 增强了图表示学习，可能改进依赖图数据的下游AI任务。

排序理由 该集群包含一篇详细介绍图对比学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的SPGCL方法通过优化正样本增强图对比学习

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该集群包含一篇详细介绍图对比学习新方法的学术论文。[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) · Lianze Shan, Ningchong Wang, Jitao Zhao, Di Jin, Dongxiao He ·

    从消息传递视角重新审视图对比学习中的正样本

    arXiv:2606.10284v1 Announce Type: new Abstract: Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream graph pre-training paradigm. It is widely recognize…

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