The widespread sharing of anonymized graph datasets for research and public use has raised new privacy concerns, as sensitive information can often be re-identified through advanced deanonymization techniques. Existing graph deanonymization methods span a variety of strategies, including structural alignment, embedding-based matching, and feature extraction combined with machine learning classifiers. However, these approaches remain grounded in graph-specific workflows. In this paper, we introduce G2TA (Graph to Table Attack), a novel attack framework that bridges graph and tabular privacy domains. By transforming anonymized graphs into semantically enriched tabular representations that capture structural patterns and neighborhood-level attribute signals, our approach enables the use of record linkage and quasi-identifier-based attacks originally developed for tabular datasets. These semantic features preserve node-to-neighborhood relationships that are crucial for identity recovery. We evaluate the framework on anonymized graphs and compare it to well-known graph-based attacks, showing that meaningful re-identification is possible, even without graph-specific algorithms. We also evaluated our methodology on differentially private graphs. Our findings underscore the importance of privacy evaluations that account for both graph-specific threats and relational tabular attacks before public release.