| --- |
| pipeline_tag: graph-ml |
| --- |
| |
| # GILT: Graph In-context Learning Transformer |
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| GILT is a novel Graph Foundational Model (GFM) that achieves **LLM-free** and **tuning-free** in-context learning on graphs. Unlike existing approaches that rely on Large Language Models or require costly per-graph tuning, GILT reframes few-shot graph learning as a token-based reasoning problem, enabling direct inference on new tasks without any parameter updates. |
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| - **Paper**: [GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning](https://huggingface.co/papers/2510.04567) |
| - **Repository**: [https://github.com/yiming421/inductnode/](https://github.com/yiming421/inductnode/) |
|
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| ## Key Features |
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| - **LLM-Free**: Works directly with numerical features without text dependency. |
| - **Tuning-Free**: Adapts to new tasks via in-context learning without gradient updates. |
| - **Multi-Task**: Unified framework for node, link, and graph classification. |
| - **Efficient**: Orders of magnitude faster than tuning-based or LLM-based methods. |
| - **Strong Performance**: State-of-the-art few-shot results across diverse benchmarks. |
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|
| ## Installation |
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|
| ```bash |
| # Create conda environment |
| conda env create -f env.yml |
| conda activate gnn |
| ``` |
|
|
| ## Quick Start |
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| ### Training |
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| To train GILT from scratch with the default configuration: |
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| ```bash |
| python train.py |
| ``` |
|
|
| ### Few-Shot Evaluation |
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| GILT requires no tuning for new tasks. You can simply provide a few labeled examples at inference time. To evaluate using a pre-trained model: |
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|
| ```bash |
| # Download checkpoint (replace with local path if downloaded manually) |
| # Multi-task evaluation |
| python train.py --use_pretrained_model true --load_checkpoint checkpoints/gilt_model.pt |
| ``` |
|
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| The model performs in-context learning without any parameter updates, directly inferring from few-shot examples. |
|
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| ## Architecture |
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| GILT consists of two main components: |
| 1. **Graph-Native Tokenization**: Converts heterogeneous graphs into unified token representations using PCA-based feature alignment and a deep linear GCN encoder. |
| 2. **In-Context Reasoning**: A Transformer-based module that performs reasoning over contextual tokens using a two-stage attention mechanism and a prototypical prediction head. |
