Declarative Explanations for Graph Neural Networks: A Demonstration

Abstract:Graph Neural Networks (GNNs) have proven effective for graph analytical tasks, yet their "black-box" nature complicates the understanding of their layer-wise behavior and hinders their application to large graphs. We demonstrate SliceGXQ, an end-to-end declarative and interactive system for querying and exploring layer-wise explanations for GNNs over large-scale graphs. In particular, SliceGXQ provides (1) an interactive graphical user interface for dataset, model, and explanation configuration; (2) a lightweight SPARQL-like parser that allows users to specify target nodes, layers, explanation modes, and constraints; and (3) an integrated execution and visualization workflow that invokes SliceGX as its underlying explanation engine. We demonstrate how users can diagnose a misclassified node, identify problematic layers and influential messages, and perform targeted model debugging, together with large-scale graph interpretation and fraud-detection analysis. Our demonstration video and source code are available at this https URL.

Machine Learning (cs.LG)

arXiv:2610.26104 [cs.LG]

(or arXiv:2610.26104v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.26104 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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