Data simulation is an established methodology in data engineering for augmenting limited datasets and safeguarding privacy, particularly for attributed graphs in domains such as finance and social networks. While existing methods successfully replicate topological and attribute distributions (statistical fidelity), we claim that such statistical similarity does not inherently ensure explainability alignment—which we define as the preservation of the underlying rationales for model decisions inherent in the data. Synthetic graphs that are statistically similar but explainability-misaligned can lead downstream models to capture spurious correlations, resulting in unreliable decision-making. To address this, we formalize the task of Explainability-Aligned Attributed Graph Generation (EAGG), which seeks to produce synthetic graphs that satisfy both statistical fidelity and explainability alignment. We propose X-AGG, a tailored framework that distills these predictive rationales into quantitative importance signals and integrates them as explicit conditions into a Variational Autoencoder architecture. By steering the generative process with explainability signals, X-AGG produces synthetic graphs that are intended to be both statistically representative and aligned with the original predictive logic. Experimental results indicate that X-AGG achieves state-of-the-art performance across traditional statistical metrics while demonstrating superior capability in preserving explainability alignment compared to existing baselines.
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