Learning Graph Quantized Tokenizers for Transformers

• URL: http://arxiv.org/abs/2410.13798v1
• Date: Thu, 17 Oct 2024 17:38:24 GMT
• Title: Learning Graph Quantized Tokenizers for Transformers
• Authors: Limei Wang, Kaveh Hassani, Si Zhang, Dongqi Fu, Baichuan Yuan, Weilin Cong, Zhigang Hua, Hao Wu, Ning Yao, Bo Long,
• Abstract summary: Graph Transformers (GTs) have emerged as a leading model in deep learning, outperforming Graph Neural Networks (GNNs) in various graph learning tasks. We introduce GQT (textbfGraph textbfQuantized textbfTokenizer), which decouples tokenizer training from Transformer training by leveraging graph self-supervised learning. By combining the GQT with token modulation, a Transformer encoder achieves state-of-the-art performance on 16 out of 18 benchmarks, including large-scale homophilic and heterophilic datasets.
• Score: 28.79505338383552
• License:
• Abstract: Transformers serve as the backbone architectures of Foundational Models, where a domain-specific tokenizer helps them adapt to various domains. Graph Transformers (GTs) have recently emerged as a leading model in geometric deep learning, outperforming Graph Neural Networks (GNNs) in various graph learning tasks. However, the development of tokenizers for graphs has lagged behind other modalities, with existing approaches relying on heuristics or GNNs co-trained with Transformers. To address this, we introduce GQT (\textbf{G}raph \textbf{Q}uantized \textbf{T}okenizer), which decouples tokenizer training from Transformer training by leveraging multi-task graph self-supervised learning, yielding robust and generalizable graph tokens. Furthermore, the GQT utilizes Residual Vector Quantization (RVQ) to learn hierarchical discrete tokens, resulting in significantly reduced memory requirements and improved generalization capabilities. By combining the GQT with token modulation, a Transformer encoder achieves state-of-the-art performance on 16 out of 18 benchmarks, including large-scale homophilic and heterophilic datasets. The code is available at: https://github.com/limei0307/graph-tokenizer

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