| --- |
| license: apache-2.0 |
| --- |
| |
| ## Overview |
| This dataset covers the encoder embeddings and prediction results of LLMs of paper 'Model Generalization on Text Attribute Graphs: Principles with Lagre Language Models', Haoyu Wang, Shikun Liu, Rongzhe Wei, Pan Li. |
|
|
| ## Dataset Description |
|
|
| The dataset structure should be organized as follows: |
|
|
| ```plaintext |
| /dataset/ |
| │── [dataset_name]/ |
| │ │── processed_data.pt # Contains labels and graph information |
| │ │── [encoder]_x.pt # Features extracted by different encoders |
| │ │── categories.csv # label name raw texts |
| │ │── raw_texts.pt # raw text of each node |
| ``` |
|
|
| ### File Descriptions |
| - **`processed_data.pt`**: A PyTorch file storing the processed dataset, including graph structure and node labels. Note that in heterophilic datasets, thie is named as [Dataset].pt, where Dataset could be Cornell, etc, and should be opened with DGL. |
| - **`[encoder]_x.pt`**: Feature matrices extracted using different encoders, where `[encoder]` represents the encoder name. |
| - **`categories.csv`**: raw label names. |
| - **`raw_texts.pt`**: raw node texts. Note that in heterophilic datasets, this is named as [Dataset].csv, where Dataset can be Cornell, etc. |
| |
| ### Dataset Naming Convention |
| `[dataset_name]` should be one of the following: |
| - `cora` |
| - `citeseer` |
| - `pubmed` |
| - `bookhis` |
| - `bookchild` |
| - `sportsfit` |
| - `wikics` |
| - `cornell` |
| - `texas` |
| - `wisconsin` |
| - `washington` |
| |
| ### Encoder Naming Convention |
| `[encoder]` can be one of the following: |
| - `sbert` (the sentence-bert encoder) |
| - `roberta` (the Roberta encoder) |
| - `llmicl_primary` (the vanilla LLM2Vec) |
| - `llmicl_class_aware` (the task-adaptive encoder) |
| - `llmgpt_text-embedding-3-large` (the embedding api text-embedding-3-large by openai) |
| |
| |
| ## Results Description |
| |
| The ./results/ folder consists of prediction results of GPT-4o in node text classification and GPT-4o-mini in homophily ratio prediction. |
| |
| ```plaintext |
| ./results/nc_[DATASET]/4o/llm_baseline # node text prediction |
| ./results/nc_[DATASET]/4o_mini/agenth # homophily ratio prediction |
| ``` |
| |
| ## Reference |
| If you find the data useful, please consider citing our paper: |
| |
| ``` |
| @inproceedings{wang2025generalization, |
| title={Generalization Principles for Inference over Text-Attributed Graphs with Large Language Models}, |
| author={Wang, Haoyu and Liu, Shikun and Wei, Rongzhe and Li, Pan}, |
| booktitle={Forty-second International Conference on Machine Learning}, |
| year={2025} |
| } |
| ``` |
| |
