Graph Structure Learning (GSL) has demonstrated considerable potential in the
analysis of graph-unknown non-Euclidean data across a wide range of domains.
However, constructing an end-to-end graph structure learning model poses a
challenge due to the impediment of gradient flow caused by the nearest neighbor
sampling strategy. In this paper, we construct a differential graph structure
learning model by replacing the non-differentiable nearest neighbor sampling
with a differentiable sampling using the reparameterization trick. Under this
framework, we argue that the act of sampling \mbox{nearest} neighbors may not
invariably be essential, particularly in instances where node features exhibit
a significant degree of similarity. To alleviate this issue, the bell-shaped
Gaussian Similarity (GauSim) modeling is proposed to sample non-nearest
neighbors. To adaptively model the similarity, we further propose Neural
Gaussian Similarity (NeuralGauSim) with learnable parameters featuring flexible
sampling behaviors. In addition, we develop a scalable method by transferring
the large-scale graph to the transition graph to significantly reduce the
complexity. Experimental results demonstrate the effectiveness of the proposed
methods.

通过使用可微分采样替代非可微邻近采样，我们构建了一种差分图结构学习模型，其中提出了钟形高斯相似性（GauSim）建模用于采样非最近邻，进一步提出具有灵活采样行为的可学习参数的神经高斯相似性（NeuralGauSim），同时通过将大规模图转换为转换图来显著降低计算复杂度。实验结果证明了所提方法的有效性。

神经高斯相似性建模用于差分图结构学习

Neural Gaussian Similarity Modeling for Differential Graph Structure  Learning

Nearest neighbor (NN) sampling provides more semantic variations than
pre-defined transformations for self-supervised learning (SSL) based image
recognition problems. However, its performance is restricted by the quality of
the support set, which holds positive samples for the contrastive loss. In this
work, we show that the quality of the support set plays a crucial role in any
nearest neighbor based method for SSL. We then provide a refined baseline
(pNNCLR) to the nearest neighbor based SSL approach (NNCLR). To this end, we
introduce pseudo nearest neighbors (pNN) to control the quality of the support
set, wherein, rather than sampling the nearest neighbors, we sample in the
vicinity of hard nearest neighbors by varying the magnitude of the resultant
vector and employing a stochastic sampling strategy to improve the performance.
Additionally, to stabilize the effects of uncertainty in NN-based learning, we
employ a smooth-weight-update approach for training the proposed network.
Evaluation of the proposed method on multiple public image recognition and
medical image recognition datasets shows that it performs up to 8 percent
better than the baseline nearest neighbor method, and is comparable to other
previously proposed SSL methods.

通过引入伪最近邻和平滑权重更新方法，改进了基于最近邻的自监督学习方法，提升了性能。