Improve dataset card: Add description, paper and code links, and sample usage

README.md

@@ -1,21 +1,24 @@
 ---
+language:
+- en
+license: cc-by-nc-sa-4.0
+size_categories:
+- 1M<n<10M
+task_categories:
+- graph-ml
+- text-classification
+pretty_name: TAGFN
 configs:
 - config_name: politifact
   data_files:
-  - "politifact/raw_text/*.parquet"
+  - politifact/raw_text/*.parquet
   default: true
 - config_name: gossipcop
   data_files:
-  - "gossipcop/raw_text/*.parquet"
+  - gossipcop/raw_text/*.parquet
 - config_name: fakeddit
   data_files:
-  - "fakeddit/raw_text/*.parquet"
-license: cc-by-nc-sa-4.0
-task_categories:
-- graph-ml
-- text-classification
-language:
-- en
+  - fakeddit/raw_text/*.parquet
 tags:
 - social
 - news
@@ -24,7 +27,20 @@ tags:
 - misinformation
 - classification
 - detection
-pretty_name: TAGFN
-size_categories:
-- 1M<n<10M
----
+---
+
+# TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs
+
+[Paper](https://huggingface.co/papers/2511.21624) | [Code](https://github.com/kayzliu/tagfn)
+
+TAGFN is a large-scale, real-world text-attributed graph dataset specifically designed for outlier detection, particularly in the context of fake news detection. It addresses the scarcity of large-scale, realistic, and well-annotated datasets for evaluating both traditional and Large Language Model (LLM)-based graph outlier detection methods. This dataset also facilitates the fine-tuning of LLMs for developing misinformation detection capabilities, serving as a valuable resource for advancing robust graph-based outlier detection and trustworthy AI.
+
+### Sample Usage
+
+To reproduce experiments, navigate to the repository and run:
+
+```bash
+bash run.sh
+```
+
+To host an LLM server locally with `vllm`, refer to the commands provided in `vllm.sh`.