A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning

2025-10-14

AI Summary

Existing graph tokenization methods exhibit limitations in hierarchical structure modeling and task adaptability: quantization strategies are often fixed or task-agnostic, leading to imbalanced structural representation and hindering dynamic multi-scale contribution adjustment without retraining the encoder. This paper proposes HQ-Graph, a Hierarchical Quantization Graph tokenization framework that enables dynamic multi-scale graph structural aggregation under a frozen encoder via a lightweight self-weighted gating mechanism, supporting task-adaptive discrete representation learning. Its core innovation lies in the organic integration of hierarchical quantization, discrete representation, learnable gating, and multi-scale aggregation. Experiments demonstrate that HQ-Graph consistently outperforms strong baselines on node classification and link prediction tasks, achieving superior performance at comparable computational cost—thereby balancing expressive power and parameter efficiency.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recent progress in language and vision foundation models demonstrates the importance of discrete token interfaces that transform complex inputs into compact sequences for large-scale modeling. Extending this paradigm to graphs requires a tokenization scheme that handles non-Euclidean structures and multi-scale dependencies efficiently. Existing approaches to graph tokenization, linearized, continuous, and quantized, remain limited in adaptability and efficiency. In particular, most current quantization-based tokenizers organize hierarchical information in fixed or task-agnostic ways, which may either over-represent or under-utilize structural cues, and lack the ability to dynamically reweight contributions from different levels without retraining the encoder. This work presents a hierarchical quantization framework that introduces a self-weighted mechanism for task-adaptive aggregation across multiple scales. The proposed method maintains a frozen encoder while modulating information flow through a lightweight gating process, enabling parameter-efficient adaptation to diverse downstream tasks. Experiments on benchmark datasets for node classification and link prediction demonstrate consistent improvements over strong baselines under comparable computational budgets.

Problem

• Develops hierarchical tokenization for non-Euclidean graph structures
• Enables task-adaptive aggregation across scales without retraining encoder
• Improves graph representation learning efficiency for downstream tasks

Innovation

• Hierarchical quantization framework for graph representation learning
• Self-weighted mechanism enables task-adaptive multi-scale aggregation
• Lightweight gating process maintains frozen encoder for efficiency