Graph Distributional Analytics: Enhancing GNN Explainability through Scalable Embedding and Distribution Analysis
Keywords: Graph Neural Networks, Explainability, Graph Distributional Analytics, Weisfeiler-Leman graph kernel, Graph embeddings, Distributional analysis, Out-of-distribution data, Model transparency, Structural features, Machine learning, Graph classification, Scalable methods, GNN interpretability, Model robustness
TL;DR: We introduce Graph Distributional Analytics, combining Weisfeiler-Leman graph kernels with distributional analysis to enhance GNN explainability by quantifying graph data distributions and identifying structural causes of misclassifications.
Abstract: Graph Neural Networks (GNNs) have achieved significant success in processing graph-structured data but often lack interpretability, limiting their practical applicability. We introduce the Graph Distributional Analytics (GDA) framework, leveraging novel combinations of scalable techniques to enhance GNN explainability. The integration of Weisfeiler-Leman (WL) graph kernels with distributional distance analysis enables GDA to efficiently quantify graph data distributions, while capturing global structural complexities without significant computational costs. GDA creates high-dimensional embeddings employing WL kernels, measures the distribution of distances from measures of categorical central tendency, and assigns distribution scores to quantify each graph's deviation from this vector We evaluate GDA on the ENZYMES, ogbg-ppa, and MalNet-Tiny datasets. Our experiments demonstrate GDA not only accurately characterizes graph distributions but also outperforms baseline methods in identifying specific structural features responsible for misclassifications. This comprehensive analysis provides deeper insights into how training data distributions affect model performance, particularly with out-of-distribution (OOD) data. By revealing the underlying structural causes of GNN predictions through a novel synergy of established techniques, GDA enhances transparency and offers a practical tool for practitioners to build more interpretable and robust graph-based models. Our framework's scalability, efficiency, and ability to integrate with various embedding methods make it a valuable addition to the suite of tools available for GNN analysis.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 12275
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