Unified graph-based framework for visual explainability in convolutional neural networks

In deep learning, understanding the decision-making processes of complex models is essential for advancing interpretability and trust in artificial intelligence systems. We introduce Causal Relational Attribution Graph (C-RAG), designed to deliver comprehensive, multi-perspective explanations of convolutional neural networks (CNNs) via a graph representation. C-RAG integrates gradient-based local attribution with global feature importance by constructing a graph-based representation that captures hierarchical feature inter-dependencies. In this framework, feature clusters are represented as graph nodes, and their interactions are quantified through combined localized and global attribution metrics, ensuring interpretable insights into model behavior. We evaluate C-RAG across diverse benchmark datasets (ImageNet, CIFAR-10, MNIST) and CNN architectures (ResNet18, VGG19, DenseNet201, LeNet), demonstrating significant advancements over state-of-the-art explainability methods in faithfulness, robustness, and computational efficiency. The proposed approach facilitates accurate spatial feature localization, robust dependency mapping, and efficient explanation generation, making it a valuable tool for critical applications such as medical imaging and autonomous systems. We provide a novel graph-based explainability framework, which bridges the gap between local and global interpretability, C-RAG addresses key limitations in existing methods, establishing a robust foundation for explainable AI in computer vision. • Graph view unifies local and global attributions. • C-RAG models feature dependencies with weighted edges. • Higher faithfulness and robustness across CNN backbones. • Tighter localization with lower explanation complexity. • Scales to real-time with efficient computation.