Neural Gaussian Similarity Modeling for Differential Graph Structure Learning

• URL: http://arxiv.org/abs/2312.09498v1
• Date: Fri, 15 Dec 2023 02:45:33 GMT
• Title: Neural Gaussian Similarity Modeling for Differential Graph Structure Learning
• Authors: Xiaolong Fan and Maoguo Gong and Yue Wu and Zedong Tang and Jieyi Liu
• Abstract summary: We construct a differential graph structure learning model by replacing the non-differentiable nearest neighbor sampling with a differentiable sampling. To alleviate this issue, the bell-shaped Gaussian Similarity (GauSim) modeling is proposed to sample non-nearest neighbors. We develop a scalable method by transferring the large-scale graph to the transition graph to significantly reduce the complexity.
• Score: 24.582257964387402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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