---
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
```