GraphToken: Graph Representation Tokenization

• GraphToken is a learned representation that encodes graph elements—nodes, edges, and substructures—into discrete or continuous tokens for deep learning.
• It uses methods like graph encoders, vector quantization, and spectral embeddings to transform graph data for transformer and LLM integration.
• GraphToken techniques underpin scalable transformers and retrieval-augmented systems across disciplines, enhancing graph reasoning and multimodal applications.

A GraphToken is a formal or learned representation that encodes parts or the entirety of a graph—nodes, edges, substructures, or higher-order patterns—into discrete or continuous tokenized units for purposes such as deep learning, LLM alignment, graph neural computation, graph–language integration, or efficient downstream reasoning and retrieval. The term denotes both the process of mapping graph data to tokenized sequences and the resultant tokens or token embeddings that capture graph structure, semantics, and context. GraphToken methods underpin scalable graph transformers, masked modeling, multimodal graph–LLMs, and retrieval-augmented systems across domains ranging from chemistry to blockchain analytics and text classification.

1. GraphTokenization: Definitions, Paradigms, and Taxonomy

GraphTokenization is the transformation of graph components—nodes, edges, motifs, subgraphs—into token representations usable by neural or symbolic architectures, such as Transformers or LLMs. This process encompasses both explicit discrete token mappings (e.g., using quantizers, vector quantization, motif or subgraph dictionaries, or semantic codebooks) and continuous token embeddings aligned with downstream models’ input spaces.

A surveyed taxonomy delineates multiple granularities (Yu et al., 2 Jan 2025):

• Node2token: Nodes are mapped to tokens via local attributes or learned embeddings, often through a graph encoder (e.g., GNN, GIN, GAT) and then aligned to the token space of the downstream model.
• Pairwise nodes2token: Tokenizes ordered (or unordered) node pairs to capture relational information, often with position-encoding reflecting graph distance.
• Group-aware nodes2token: Subgraphs or communities are aggregated and encoded as group tokens, leveraging attention or pooling to form higher-level semantic units (e.g., motifs, functional groups).
• Holistic nodes2token: The entire graph (or a substantial subgraph) is encoded as a single comprehensive token or as a specialized token sequence.

Tokenization approaches vary between domains and tasks:

• Graphs for Transformers: Nodes/edges or neighborhoods are tokenized as input units for graph transformers (Kim et al., 2022, Chen et al., 2023, Zhou et al., 19 Dec 2024).
• Graphs for LLMs: Graphs are mapped to tokens (or embeddings aligned with language tokens) to enable structured reasoning in LLM architectures (Perozzi et al., 8 Feb 2024, Yu et al., 2 Jan 2025, Wang et al., 5 Mar 2025).
• Graph-based RAG: Graph and concept tokenization is crucial for token-efficient retrieval in retrieval-augmented generation (Xiao et al., 23 Sep 2025).

2. Architectures and Methodologies for GraphToken Construction

The construction of GraphTokens involves both basic mathematical transformations and learning-based compression or semantic alignment mechanisms:

• Local, Motif, and Subgraph Tokenization: Direct mappings from nodes or motifs to tokens use atom types, bond types, functional groups (as detected by chemical grammars or BRICS/CyPh algorithms), or k-hop neighborhood embeddings (Liu et al., 2023, Chen et al., 20 Jun 2024). Motif-level VQVAE encoders and vector-quantized tokenizers are used to discretize substructures.
• Graph Encoder-based Tokenization: Pretrained or concurrently learned GNNs, such as GIN or GAT, produce per-node (or per-motif) feature vectors, which are then quantized or aligned with downstream model spaces (Wang et al., 17 Oct 2024, Wang et al., 5 Mar 2025). Residual vector quantization hierarchies (RVQ) further compress/quantize node features, generating compact token sets (Wang et al., 17 Oct 2024).
• Alignment with LLM Token Spaces: To bridge modality gaps with LLMs, alignment modules utilize multi-head cross-attention, projection heads, or codebook compression to produce tokens compatible with frozen LLM embeddings (Perozzi et al., 8 Feb 2024, Wang et al., 5 Mar 2025).
• Global Tokens and Spectral Embeddings: Global tokens (e.g., Graph Spectral Token (Pengmei et al., 8 Apr 2024)) inject spectral, positional, or structural invariants—such as Laplacian eigenvalue transforms—into special Transformer input tokens (e.g., the [CLS] token), enhancing global inductive bias.
• Token Sequences via Graph Traversal or Neighborhood Aggregation: Sequences are generated by traversing k-NN or similarity graphs, random walks (including non-backtracking and neighborhood-jump walks), or ordered aggregations of multi-hop neighborhoods (Chen et al., 2023, Zhou et al., 19 Dec 2024, Chen et al., 12 Feb 2025).
• Canonicalization and 3D Geometry: For 3D molecular graphs, canonical labeling combined with $SE(3)$-invariant spherical coordinate representations enable fully reversible, unique, and geometry-aware tokenization (Li et al., 19 Aug 2024).

Key LaTeX expressions and algorithmic highlights:

• Token alignment: $$T_i = GM(h_i) + \text{Align}_{\text{LLM}}(T_i^{\mathrm{text}})$$
• Multi-level aggregation:

$$T_i^{(l)} = \text{concat}\bigl[ \{T_j^{(l-1)} | j \in \mathcal{N}_1(i)\}, \ldots, \{T_j^{(l-1)} | j \in \mathcal{N}_m(i)\} \bigr]$$

• Spectral token formation (Graph Spectral Token): $z_0^{(0)} = s \odot (W_2 \lambda)$, with

$$s_j = \frac{\exp(W_1 g(\theta \cdot \lambda)_j)}{\sum_j \exp(W_1 g(\theta \cdot \lambda)_j)}$$

3. GraphToken in Graph Transformers

GraphTokens provide the foundation for recent graph transformer architectures, which circumvent the limitations of MPNNs—most notably over-smoothing, over-squashing, and limited receptive fields (Kim et al., 2022, Chen et al., 2023, Zhou et al., 19 Dec 2024).

Key strategies include:

• Node/Edge as Tokens: Every node and edge is treated as a token, augmented with node identifiers (e.g., orthonormal random vectors, Laplacian eigenvectors) and type tags (Kim et al., 2022).
• Multi-hop Neighborhood Tokens: Hop2Token and analogous modules aggregate k-hop features to generate token sequences per node (Chen et al., 2023).
• Mixed Walk and Pretrained Global Tokens: Multi-token representations composed of random-walk-based tokens, hop-tokens, and self-supervised pretrain tokens (e.g., SGPM-token, as in Tokenphormer (Zhou et al., 19 Dec 2024)) form highly expressive, diverse contexts for transformer processing.
• Hierarchical Tokenization: Hierarchical graph tokenizers (e.g., HIGHT (Chen et al., 20 Jun 2024)) integrate atom, motif, and molecule-level tokens for molecule–language alignment, employing multiple VQVAE-based codebooks and explicit Laplacian positional encoding with learned adapters for each token type.
• Spectral Tokenization: Replacing generic [CLS] tokens with graph spectral tokens (computed from Laplacian spectra via kernel transformations) allows transformer models (e.g., GraphTrans-Spec, SubFormer-Spec) to capture global structure (Pengmei et al., 8 Apr 2024).
• Token Sequence Augmentation: SwapGT expands token diversity by iterative “token swapping,” sampling tokens from neighbors’ token sets, thereby increasing both the reach and informativeness of the constructed sequences (Chen et al., 12 Feb 2025).

These architectures universally deploy transformers over sequences of GraphTokens, using multi-head self-attention and attention-based readouts to integrate multi-scale, structurally aware information.

4. GraphTokens for LLM Alignment and Multimodal Graph–LLMs

GraphTokens have become central to interfacing graph-structured data with large (frozen) LLMs (Perozzi et al., 8 Feb 2024, Yu et al., 2 Jan 2025, Wang et al., 5 Mar 2025). The principal methodology is as follows:

• Token Embedding via Graph Encoders: A dedicated graph encoder (commonly using GNN variants) learns feature representations for graph sub-elements. These are then projected or quantized into token embeddings compatible with the LLM input.
• Parameter-Efficient Integration: Only the graph encoder and projection head are trained, while the LLM parameters are kept frozen—a design that preserves the pretraining advantages of the LLM and minimizes parameter count (Perozzi et al., 8 Feb 2024, Wang et al., 5 Mar 2025).
• Prompt Augmentation via Graph Tokens: Encoded graph tokens are prepended or inserted into the LLM prompt sequence, allowing the model to process structured data without flattening it to text. This approach has demonstrated improvements of up to 73% accuracy on node, edge, and graph-level reasoning tasks over traditional serialization or zero-shot/few-shot methods (Perozzi et al., 8 Feb 2024).
• Cross-modal Alignment: In molecular settings, Graph2Token applies contrastive pretraining over molecule–text pairs and uses a cross-attention module to align graph tokens with the compressed vocabulary of the LLM; molecular IUPAC names are included in prompts to further boost alignment (Wang et al., 5 Mar 2025).
• Token-Sequence Design in Molecular LLMing: In frameworks such as GraphT5, cross-token attention modules explicitly relate 2D graph node embeddings and SMILES token embeddings, producing “GraphTokens” that underpin superior sequence modeling for tasks like IUPAC name prediction and molecule captioning (Kim et al., 7 Mar 2025).

5. Evaluation, Challenges, and Benchmarks

While GraphTokens have demonstrated efficacy in both unimodal and multimodal architectures, several critical challenges and evaluation insights have emerged:

• Current Benchmarks and Task Sufficiency: Many existing graph–language benchmarks (e.g., node classification on standard text-attributed graphs) are semantically sufficient—unimodal text models achieve comparable performance to multimodal GLMs, revealing that such benchmarks do not meaningfully probe structural integration (Petkar et al., 28 Aug 2025).
• Structural Reasoning Gap: When tasks demand multi-hop, compositional, or topological reasoning (as in the CLeGR benchmark (Petkar et al., 28 Aug 2025)), GLMs—even those using explicit GraphTokens—cannot match the performance gains seen in text-only tasks, indicating that the inclusion of graph structure is not fully leveraged.
• Alignment and Position Challenges: The mapping from discrete graph structure (which lacks inherent sequential order) to tokens suitable for sequence-based models (with strong positional inductive bias) necessitates explicit position encoding (distance-based, Laplacian, or traversal-order).
• Token Diversity and Augmentation: Limited token diversity (e.g., using only first-order k-NN neighborhoods) restricts expressive power; strategies such as token swapping, mixed multi-hop neighborhoods, and group-aware tokens are required for robust representation (Chen et al., 12 Feb 2025).
• Parameter Efficiency and Scalability: Techniques such as RVQ-based compression and token modulation are necessary to make transformer-based models feasible on large-scale graphs, drastically reducing the memory footprint (30× to 270×) while maintaining or improving predictive accuracy (Wang et al., 17 Oct 2024).
• Fairness and Robustness: Tokenization strategies and alignment modules must avoid introducing or amplifying biases, particularly in attribute-rich, imbalanced, or dynamic graphs (Yu et al., 2 Jan 2025).

6. Applications and Impact in Domain-specific and General Settings

GraphToken methods underpin and enable a wide array of applications:

• Blockchain and Token Ecosystems: Comprehensive graph analyses in blockchain contexts (e.g., EOSIO, Ethereum) use token graphs to map creator, holder, and transfer structures, detect manipulation (fake-token detection with ATTNF/MTTQF metrics), and model compositional relationships of tokenized assets (Zheng et al., 2022, Harrigan et al., 3 Nov 2024).
• Chemistry, Bioinformatics, and Molecular Generation: In molecular informatics, GraphTokens facilitate masking-based pretraining, graph–language alignment, property prediction, molecular captioning, and geometry-constrained molecular generation (Liu et al., 2023, Chen et al., 20 Jun 2024, Li et al., 19 Aug 2024, Wang et al., 5 Mar 2025, Kim et al., 7 Mar 2025).
• Text Mining and Information Retrieval: Token-level graphs built from PLM-derived token embeddings support robust short text classification, overcoming data sparsity and context-dependence limitations (Donabauer et al., 17 Dec 2024).
• Retrieval-Augmented LLMs: In RAG, token-efficient graph construction (e.g., TERAG (Xiao et al., 23 Sep 2025)) reduces LLM inference cost by extracting concise concept and passage nodes, using personalized PageRank for retrieval, and minimizing unnecessary token generation without sacrificing QA accuracy.
• Decentralized Optimization and Networked Learning: Token-based communication and consensus procedures (single/multi-token random-walk protocols) provide privacy and communication-complexity advantages in decentralized optimization (Hendrikx, 2022).

7. Future Directions and Open Problems

Major research frontiers center on theoretical, methodological, and practical enhancements:

• Expressivity and Invariance: Formal paper of expressiveness, permutation/geometric invariance, and representation power when aligning graph tokens with sequential models.
• Dynamic and Heterogeneous Graphs: Developing tokenization and sequence-construction methodologies that scale to evolving, multi-relational, and multi-typed graphs.
• Instruction Design and Prompting: Integration of domain-specific instructions and multimodal prompting for more generalized graph–LLM reasoning (Yu et al., 2 Jan 2025).
• Multimodal and Hierarchical Fusion: Refined fusion mechanisms (beyond cross-token attention) for multi-level and multimodal data; incorporating 3D geometry and temporal structure.
• Evaluation Ecosystem: The need for structurally challenging, multimodal benchmarks (e.g., CLeGR) to drive real progress in joint graph–language reasoning (Petkar et al., 28 Aug 2025).
• Scaling and Memory Efficiency: Continued focus on highly compressed (quantized or hierarchical) tokenization to enable transformer models on massive graphs without resource bottlenecks (Wang et al., 17 Oct 2024).
• Minimizing Hallucination and Improving Factuality: Combining explicit structural tokens with augmented textual data to reduce hallucination—especially in molecule–language alignment (Chen et al., 20 Jun 2024).

GraphTokenization remains a rapidly evolving field critically enabling the integration of structured, relational, and multimodal information within universal neural architectures, further expanding the reach and capability of foundation models across scientific, linguistic, and networked systems.