Structural Compactness as a Complementary Criterion for Explanation Quality
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arXiv:2603.29491v1 Announce Type: new Abstract: In the evaluation of attribution quality, the quantitative assessment of explanation legibility is particularly difficult, as it is influenced by varying shapes and internal organization of attributions not captured by simple statistics. To address this issue, we introduce Minimum Spanning Tree Compactness (MST-C), a graph-based structural metric that captures higher-order geometric properties of attributions, such as spread and cohesion. These components are combined into a single score that evaluates compactness, favoring attributions with salient points spread across a small area and spatially organized into few but cohesive clusters. We show that MST-C reliably distinguishes between explanation methods, exposes fundamental structural diff
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Abstract:In the evaluation of attribution quality, the quantitative assessment of explanation legibility is particularly difficult, as it is influenced by varying shapes and internal organization of attributions not captured by simple statistics. To address this issue, we introduce Minimum Spanning Tree Compactness (MST-C), a graph-based structural metric that captures higher-order geometric properties of attributions, such as spread and cohesion. These components are combined into a single score that evaluates compactness, favoring attributions with salient points spread across a small area and spatially organized into few but cohesive clusters. We show that MST-C reliably distinguishes between explanation methods, exposes fundamental structural differences between models, and provides a robust, self-contained diagnostic for explanation compactness that complements existing notions of attribution complexity.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.29491 [cs.AI]
(or arXiv:2603.29491v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2603.29491
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohammad Mahdi Mesgari [view email] [v1] Tue, 31 Mar 2026 09:36:52 UTC (23,903 KB)
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