Folders and files
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A curated list of Graph Transformers (Single & Multi-modal) — covering both Traditional and LLM-based Graph Transformers (📝 Legend: 🕸️ Single-Modal, 🌈 Multi-Modal).
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🌈 NTSFormer: A Self-Teaching Graph Transformer for Multimodal Isolated Cold-Start Node Classification (AAAI 2026) - Hu, et al. [Paper] [Code]
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🌈 Multimodal Graph Transformer for Multimodal Question Answering (EACL 2023) - He, et al. [Paper]
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🕸️ SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations (NeurIPS 2023) - Wu, et al. [Paper] [Code]
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🕸️ HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer (WWW 2023) - Mao, et al. [Paper] [Code]
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🕸️ [GraphGPS] Recipe for a General, Powerful, Scalable Graph Transformer (NeurIPS 2022) - Rampášek, et al. [Paper] [Code]
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🕸️ [Graphormer] Do Transformers Really Perform Bad for Graph Representation? (NeurIPS 2021) - Ying, et al. [Paper] [Code]
- 🕸️ DGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs (AAAI 2026) - Li, et al. [Paper]