When Structure Doesn't Help: LLMs Read Text-Attributed Graphs as Effectively as We Expected

In a surprising twist for the field of graph machine learning, new research suggests that large language models (LLMs) may not need explicit structural information to understand text-attributed graphs. A study from researchers at Stony Brook University and Caltech systematically s the foundational assumption that graph structure is inherently beneficial for LLM-based graph reasoning, finding instead that semantic content alone often suffices—and sometimes even outperforms structure-aware approaches. This represents a significant departure from traditional graph learning paradigms that have dominated the field for over a decade, where Graph Neural Networks (GNNs) have been the standard approach for combining node features with relational information. could reshape how researchers approach graph foundation models in the era of powerful language models, potentially simplifying architectures and reducing computational overhead for many real-world applications.

The research ology involved systematic experiments across multiple graph types, encoding templates, and modeling paradigms. The team evaluated six real-world text-attributed graph datasets spanning diverse domains including citation networks (Cora, Citeseer, Pubmed), web page graphs (School), Wikipedia articles (Roman Empire), and e-commerce platforms (Amazon Ratings), covering both homophilic and heterophilic structural patterns. They used Vicuna-7b-v1.5 as their primary LLM backbone and conducted controlled ablation studies to isolate the contribution of structural components. For template-based encoding, they compared LLaGA's Neighborhood Detail (ND) template—which incorporates Laplacian-based positional encodings—against two structure-agnostic variants: Hop Neighbor (HN), which randomly samples k-hop neighbors, and Center Only (CO), which provides only the central node description. For GNN-based encoding, they evaluated the GraphToken framework with different backbones including GCN, GAT, and GIN, comparing them against a simple multi-layer perceptron (MLP) baseline.

Consistently revealed that structural information provides marginal or even negative gains when rich semantic node features are present. In template-based experiments, the structure-free HN and CO variants performed competitively with or better than the structure-aware ND template across both node classification and link prediction tasks. On heterophilic graphs like School, including structural embeddings actually degraded performance, with the CO variant achieving 91.13% accuracy compared to ND's 66.43%. In GNN-based experiments, replacing GNNs with simple MLPs while keeping other components constant yielded comparable performance, with MLPs achieving 76.26% average accuracy across datasets versus 67.50% for GCNs. The researchers also extended their investigation to molecular graphs (BACE, BBBP, HIV) and geometric deep learning tasks (Davis Drug-Target Interaction dataset), finding similar patterns where sequence-based semantic representations performed comparably to structure-based encodings.

Of these are substantial for both research and practical applications. The study suggests that LLMs primarily treat input graphs as unordered sets, relying more heavily on the content of node sequences than on underlying graph topology. This s the prevailing assumption that structural encoding is critical for LLM-based graph modeling and calls for a rethinking of how—or whether—structure should be incorporated into future graph foundation models. The research aligns with recent critiques of graph benchmarks by Bechler-Speicher et al. (2025), who argue that existing datasets often fail to reflect real-world relational complexity. For practitioners, could lead to simpler, more efficient architectures that prioritize meaningful textual context over handcrafted structural encodings, potentially reducing computational requirements while maintaining or even improving performance on many real-world graph tasks.

Despite these compelling , the study acknowledges several limitations and areas for future investigation. The researchers note that their may not generalize to all graph types, particularly those where structural information is truly intrinsic rather than semantically induced. They also observed that certain graph transformations, like Graph Diffusion Convolution (GDC), which captures longer-range dependencies, could provide structured yet minimal signals that LLMs might exploit more effectively. Additionally, the study primarily focused on text-attributed graphs with rich semantic content; graphs with sparse or non-textual features might still benefit from structural encoding. The researchers call for more work on designing benchmarks that genuinely require structural reasoning and on developing s to better align graph structure with LLM capabilities when such alignment proves necessary. These limitations notwithstanding, the research provides a strong empirical foundation for understanding LLM-graph interactions and underscores the importance of effective node sequencing over structural encodings in advancing LLM performance on text-attributed graph tasks.