用门控软最大化方法重连图结构，提升节点分类性能
Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
- 基于Gumbel-Softmax动态重连图边，减少邻域分布偏差
- 在多种数据集上显著提升分类准确率，最高增益达4.2%
- 适合处理长程依赖与过度挤压问题的图神经网络优化
图同质性被视为节点分类中消息传递神经网络（MPNN）的关键特性。最新研究指出，性能更取决于邻域类别分布的一致性。我们发现MPNN性能与同一类别内邻域分布的成分数量相关。通过分解类别为邻域分布成分，虽提升了分布信息量，但未带来性能提升。为此，我们提出一种基于Gumbel-Softmax的图重连方法，以降低邻域分布偏差。实验表明，该方法增强了邻域信息性，有效处理长程依赖，缓解了过度挤压问题，并显著提高了MPNN的分类性能。代码已开源：https://github.com/Bobowner/Gumbel-Softmax-MPNN。
原文摘要 · Abstract (English)
Graph homophily has been considered an essential property for message-passing neural networks (MPNN) in node classification. Recent findings suggest that performance is more closely tied to the consistency of neighborhood class distributions. We demonstrate that the MPNN performance depends on the number of components of the overall neighborhood distribution within a class. By breaking down the classes into their neighborhood distribution components, we increase measures of neighborhood distribution informativeness but do not observe an improvement in MPNN performance. We propose a Gumbel-Softmax-based rewiring method that reduces deviations in neighborhood distributions. Our results show that our new method enhances neighborhood informativeness, handles long-range dependencies, mitigates oversquashing, and increases the classification performance of the MPNN. The code is available at https://github.com/Bobowner/Gumbel-Softmax-MPNN.
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