| | --- |
| | 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} |
| | } |
| | ``` |
| | |
