{"slug": "self-explainable-multi-label-graph-neural-network-for-correlated-evidence", "title": "Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution", "summary": "Researchers introduced SEMGNN, a self-explainable multi-label graph neural network that simultaneously classifies multi-labeled nodes and identifies edges contributing to each label, integrating training-time interpretation. The model outperforms post-hoc explainers by modeling label-dependent evidence sharing, achieving competitive predictive performance with more faithful explanations on synthetic and real-world networks.", "body_md": "arXiv:2608.27574v1 Announce Type: new\nAbstract: 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 learning methods exist, but none of them integrate training-time interpretation capability. While post-hoc graph explainers have been developed, they do not explicitly model label-dependent evidence sharing in multi-label graph learners, especially when label pairs are weakly or negatively associated. As a result, post-hoc approaches may miss how evidence should be shared or separated across different labels. This paper advances a new end-to-end self-explainable multi-label graph neural network (SEMGNN), which aims to simultaneously classify multi-labeled nodes and identify edges significantly contributing to each target node w.r.t. predicted labels. Different from post-hoc methods, SEMGNN jointly learns a predictor and a sparse edge-mask explainer within a unified framework and training objective. Label-label correlations are used to improve multi-label node classification and enhance individual label explanations, so that different labels of a node can be supported by distinct yet coherent structural and/or correlated evidence. Experiments and comparisons on synthetic and real-world multi-label networks, in social networking, entertainment, and life sciences, show that SEMGNN achieves competitive or improved predictive performance while providing more faithful and compact label-conditioned explanations.", "url": "https://wpnews.pro/news/self-explainable-multi-label-graph-neural-network-for-correlated-evidence", "canonical_source": "https://arxiv.org/abs/2608.27574", "published_at": "2026-08-31 04:00:00+00:00", "updated_at": "2026-08-31 04:23:15.172871+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "artificial-intelligence"], "entities": ["SEMGNN"], "alternates": {"html": "https://wpnews.pro/news/self-explainable-multi-label-graph-neural-network-for-correlated-evidence", "markdown": "https://wpnews.pro/news/self-explainable-multi-label-graph-neural-network-for-correlated-evidence.md", "text": "https://wpnews.pro/news/self-explainable-multi-label-graph-neural-network-for-correlated-evidence.txt", "jsonld": "https://wpnews.pro/news/self-explainable-multi-label-graph-neural-network-for-correlated-evidence.jsonld"}}