Transparency and accountability have become major concerns for black-box machine learning (ML) models. Proper explanations for the model behavior increase model transparency and help researchers develop more accountable models. Graph neural networks (GNN) have recently shown superior performance in many graph ML problems than traditional methods, and explaining them has attracted increased interest. However, GNN explanation for link prediction (LP) is lacking in the literature. LP is an essential GNN task and corresponds to web applications like recommendation and sponsored search on web. Given existing GNN explanation methods only address node/graph-level tasks, we propose Path-based GNN Explanation for heterogeneous Link prediction (PaGE-Link) that generates explanations with connection interpretability, enjoys model scalability, and handles graph heterogeneity. Qualitatively, PaGE-Link can generate explanations as paths connecting a node pair, which naturally captures connections between the two nodes and easily transfer to human-interpretable explanations. Quantitatively, explanations generated by PaGE-Link improve AUC for recommendation on citation and user-item graphs by 9 - 35% and are chosen as better by 78.79% of responses in human evaluation.
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
Yonsei University（延世大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）
PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks
Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia（信息与通信工程系，穆尔西亚大学）；Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London（遗传学与基因组医学，儿童健康大奥蒙街研究所，伦敦大学学院）；Department of Neurodegenerative Diseases, UCL Institute of Neurology（神经退行性疾病部门，伦敦大学学院神经学研究所）；NIMGenetics, Genómica y Medicina, SL, Madrid, Spain（NIMGenetics，基因组学与医学，SL，马德里，西班牙）
Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；AIRI；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；KAUST（阿卜杜拉国王科技大学）；Skoltech（斯科尔泰克科技大学）
Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data
Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany（慕尼黑路德维希-马克西米利安大学儿童与青少年精神病学系医学成像人工智能（AI-Med））
Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions
Department of Informatics, University of Oslo（奥斯陆大学信息学院）；Integreat – Norwegian Centre for Knowledge-driven Machine Learning（挪威知识驱动机器学习中心）；TRUST – The Norwegian Centre for Trustworthy AI（挪威可信人工智能中心）
Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network
Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Hefei University of Technology（合肥工业大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
