这种被称为“过度挤压(over-squashing)”的现象在之前的一归因于图瓶颈(Bottleneck)。 这篇论文对 GNN中的过度挤压现象进行了一种新的解读,并且分析了它是如何从图瓶颈中产生的。大致来说,作者引入了一种基于边的组合曲率,并证明负曲率的边是造成过度挤压问题的原因。作者通过实验测试了一种基于曲率的图重新布线(...
在图神经网络(GNN)中,"over-smoothing" 和 "over-squashing" 是两种不同的问题,它们影响网络的性能和学习能力。 1. Over-Smoothing: 定义:Over-smoothing 是指随着图神经网络层数的增加,节点特征变得越来越相似,最终在高层次上收敛到一个相似或相同的状态。这导致不同节点之间的特征区分度降低,使得GNN难以捕捉到...
Repository of the paper "On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks" published in ACM CIKM 2023 - jhonygiraldo/SJLR
文章目录 1 前言 2 Over Suqashing 4 总结 Reference 论文地址:https://openreview.net/pdf?id=i80OPhOCVH2 源码:bottleneck 来源:ICLR, 2021 关键词:GNN, Over-Squashing 1 前言 该论文针对GNN中的远距离信息的传播问题提出了一个新的解释。文中讨论的问题是:GNN在聚集/利用远距离结点的... ...
However, in the standard RNN, the input vectors only implicitly interact through the nonlinearity (squashing) function. 然而,在标准的RNN中,输入向量仅通过非线性(压扁)函数进行隐式交互。 A more direct, possibly multiplicative, interaction would allow the model to have greater interactions between the ...
a政府正在努力解决这些问题 The government is solving these problems diligently [translate] a我必须陪她 I must accompany her [translate] athe Visual Squash and Ref raming. Conceptually, it has been heavily influenced by the work of Fritz 视觉南瓜和Ref raming。 概念上,它被弗里茨工作沉重影响了 [...
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Paper tables with annotated results for Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
hindering deep representation learning and information propagation from distant nodes. Our work reveals that over-smoothing and over-squashing are intrinsically related to the spectral gap of the graph Laplacian, resulting in an inevitable trade-off between these two issues, as they cannot be alleviate...