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Graph neural networks (GNNs) have been successfully deployed across numerous practical domains, yet the precise mechanisms underlying their operations remain largely opaque. In response to this lack of clarity and to encourage the deployment of reliable AI systems, a variety of GNN explanation tools have been developed. Nonetheless, current interpretation techniques frequently encounter severe bottlenecks, including: (1) explanations that are restricted to localized individual instances; (2) poor generalizability when applied to unfamiliar graph structures; (3) the risk of creating structurally invalid graphs; and (4) confinement to specific task domains (such as only node or only graph classification). To resolve these deficiencies, we introduce a novel interpretation framework, GAN-GNNExplainer, which integrates a generator to construct explanations alongside a discriminator to supervise the production stage, thereby enhancing output dependability. Although advantageous, GAN-GNNExplainer still exhibits limitations in producing highly faithful explanations and shows suboptimal performance when tested on empirical datasets. To rectify these weaknesses, we present ACGAN-GNNExplainer, an advanced iteration that enhances GAN-GNNExplainer by utilizing a more robust discriminator to continuously monitor the generation phase, which yields explanatory outputs that are both dependable and faithful. Comprehensive empirical evaluations conducted on both artificial and empirical graph datasets confirm that our proposed methodologies outperform conventional GNN explanation frameworks.

Explainable artificial intelligence has generated extensive technical and human-centered evaluation research, yet substantial uncertainty remains about what explanation evaluations actually demonstrate. This umbrella review synthesizes review-level evidence concerning the evaluation of fidelity, faithfulness, stability, robustness, comprehensibility, cognitive accessibility, decision usefulness, human trust, trust calibration, and reliance. Rather than reviewing explanation algorithms as methods, the article examines the constructs, metrics, study designs, and validity assumptions through which explanation quality has been assessed. The synthesis distinguishes technical correspondence with model behavior from human judgments of plausibility, understanding, usefulness, and satisfaction. It further separates human trust from system trustworthiness and behavioral reliance from beneficial decision support. Review-level evidence indicates that technical metrics are heterogeneous, terminology is inconsistently operationalized, and stable or plausible explanations are not necessarily faithful. Human-centered evaluations frequently rely on self-reported understanding, satisfaction, or trust, while objective comprehension, cognitive load, decision quality, appropriate reliance, overreliance, and underreliance receive less consistent assessment. Evidence linking technical explanation properties to downstream human and institutional outcomes remains limited, particularly across stakeholder groups, realistic decision contexts, repeated use, and changing system conditions. The umbrella synthesis therefore rejects single-metric interpretations of explanation quality and organizes evaluation as a multidimensional evidence problem involving technical validity, cognitive accessibility, decision consequences, calibrated trust, reliance behavior, accountability, and contestability. Its principal contribution is a consolidated evaluation agenda that clarifies what existing measures may support, what they do not establish, and which methodological connections require stronger user-centered, task-specific, longitudinal, and socio-technical validation.