GraphXAI provides functions that visualize explanations produced by GNN explainability methods. Users can compare both node- and graph-level explanations. In addition, function implementations for visualization are parameterized, allowing users to change colors and weight interpretation. Functions are compatib...
As Graph Neural Networks (GNNs) have shown superiority over various network analysis tasks, their explainability has also gained attention from both academia and industry. However, despite the increasing number of GNN explanation methods, there is currently neither a fine-grained taxonomy of them, ...
Explainability methods for graph convolutional neural networks. In CVPR, 2019. [45] Petar Veliˇckovi´c, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017. [53] Zhitao Ying, Dylan Bourgeois, ...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
SubgraphX:On explainability of graph neural networks via subgraph explorations. ICML'21 PGExplainer:Parameterized explainer for graph neural network. NIPS‘20 GNNExplainer: Generating explanations for graph neural networks. NIPS'19 GraphMask:Interpreting graph neural networks for NLP with differentiable edge...
可解释性(Explainability) 增加推荐系统的可解释性可以增强用户感知的透明度、说服力和可信度,也会方便调试与完善系统。 公平性(Fairness) 公平性也分成两部分,分别是用户公平性和项目公平性。对用户来说,推荐的产品在不同用户与群体之间不应该存在算法偏见;而对于项目来说,不同项目的曝光度不应该存在过大的差异。总...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 2.6 Explainability 3. Applications 3.1 Physics 3.2 Chemistry an...
How ML Model Explainability Accelerates the AI Adoption Journey for… A Comprehensive Guide to Convolutional Neural Networks Learn how to design, measure and implement trustworthy A/B tests… Expert Insights on Developing Safe, Secure, and Trustworthy AI Frameworks ...
Wu S, Sun F, Zhang W, Xie X, Cui B (2022) Graph neural networks in recommender systems: a survey. ACM Comput Surveys 55(5):1–37 Article MATH Google Scholar Yuan H, Yu H, Gui S, Ji S (2022) Explainability in graph neural networks: A taxonomic survey. IEEE Trans Pattern Anal ...
GKAN's design inherently provides clear insights into the model's decision-making process, eliminating the need for post-hoc explainability techniques. This paper discusses the methodology, performance, and interpretability of GKAN, highlighting its potential for applications in domains where interpretabili...