Researchers have identified a critical ambiguity in evaluating graph neural network (GNN) explanations, specifically concerning the concept of 'deletion fidelity.' This metric typically involves removing top-ranked edges to assess the impact on model output, but the act of 'deleting an edge' can be interpreted in two distinct ways: structural deletion, which alters the graph's overall structure, and message suppression, which blocks an edge's message without changing the graph context. The study found that these two interventions only coincide under specific conditions, such as additive message passing with edge-set-independent coefficients. When these conditions are not met, the choice of intervention can significantly affect the evaluation of edge attributions, leading to different conclusions about their fidelity. AI
IMPACT Clarifies a fundamental ambiguity in evaluating GNN explanations, potentially leading to more robust and reliable interpretation methods for these models.
RANK_REASON Academic paper detailing a novel finding about GNN explanation evaluation methods. [lever_c_demoted from research: ic=1 ai=1.0]
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