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license: apache-2.0
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---
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license: apache-2.0
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---
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## Overview
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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.
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## Dataset Description
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The dataset structure should be organized as follows:
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```plaintext
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/dataset/
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│── [dataset_name]/
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│ │── processed_data.pt # Contains labels and graph information
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│ │── [encoder]_x.pt # Features extracted by different encoders
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│ │── categories.csv # label name raw texts
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│ │── raw_texts.pt # raw text of each node
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```
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### File Descriptions
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- **`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.
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- **`[encoder]_x.pt`**: Feature matrices extracted using different encoders, where `[encoder]` represents the encoder name.
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- **`categories.csv`**: raw label names.
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- **`raw_texts.pt`**: raw node texts. Note that in heterophilic datasets, this is named as [Dataset].csv, where Dataset can be Cornell, etc.
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### Dataset Naming Convention
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`[dataset_name]` should be one of the following:
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- `cora`
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- `citeseer`
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- `pubmed`
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- `bookhis`
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- `bookchild`
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- `sportsfit`
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- `wikics`
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- `cornell`
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- `texas`
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- `wisconsin`
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- `washington`
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### Encoder Naming Convention
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`[encoder]` can be one of the following:
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- `sbert` (the sentence-bert encoder)
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- `roberta` (the Roberta encoder)
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- `llmicl_primary` (the vanilla LLM2Vec)
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- `llmicl_class_aware` (the task-adaptive encoder)
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- `llmgpt_text-embedding-3-large` (the embedding api text-embedding-3-large by openai)
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## Results Description
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The ./results/ folder consists of prediction results of GPT-4o in node text classification and GPT-4o-mini in homophily ratio prediction.
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```plaintext
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./results/nc_[DATASET]/4o/llm_baseline # node text prediction
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./results/nc_[DATASET]/4o_mini/agenth # homophily ratio prediction
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```
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