English(EN) Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution

新型SEMGNN模型通过集成可解释性增强多标签图学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了一种新的端到端自解释多标签图神经网络（SEMGNN），旨在同时对多标签节点进行分类并识别有助于每次预测的边。与事后方法不同，SEMGNN在一个框架和训练目标中集成了预测器和稀疏边掩码解释器。这种方法利用标签-标签相关性来增强节点分类准确性和解释的可解释性，确保节点的不同标签由不同但连贯的证据支持。在合成和真实网络上的实验表明，SEMGNN在提供更忠实和紧凑的标签条件解释的同时，实现了具有竞争力的预测性能。 AI

影响引入了一种新颖的图神经网络方法，提高了多标签分类任务的可解释性。

排序理由该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yingqi Feng, Yufei Tang, Min Shi, Xingquan Zhu · 2026-08-31 04:00

用于相关证据归因的自解释多标签图神经网络

arXiv:2608.27574v1 Announce Type: new Abstract: Multi-label graph learning intends to capture the intrinsic complexity of real-world applications, where one sample is often related to multiple groups or consists of multiple objects. To date, a handful of multi-label graph learnin…

本文如何被排名

报道来源 [1]

用于相关证据归因的自解释多标签图神经网络

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