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The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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:
/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:
coraciteseerpubmedbookhisbookchildsportsfitwikicscornelltexaswisconsinwashington
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.
./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}
}
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