Add graph-ml pipeline tag and paper link (#1)

- Add graph-ml pipeline tag and paper link (2806b13ad949918012d57e8724fef8684429d273)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,19 +1,25 @@
 ---
-license: apache-2.0
-tags:
-  - graph-neural-networks
-  - histopathology
-  - self-supervised-learning
-  - pytorch-geometric
-  - graph-representation-learning
 datasets:
-  - graph-tcga-brca
+- ogutsevda/graph-tcga-brca
 library_name: pytorch
+license: apache-2.0
+pipeline_tag: graph-ml
+tags:
+- graph-neural-networks
+- histopathology
+- self-supervised-learning
+- pytorch-geometric
+- graph-representation-learning
 ---
 
 # GrapHist: Graph Self-Supervised Learning for Histopathology
 
-This repository contains the pre-trained model from [GrapHist](https://github.com/ogutsevda/graphist). Pre-trained on the [graph-tcga-brca](https://huggingface.co/datasets/ogutsevda/graph-tcga-brca) dataset, it employs an **ACM-GIN** (Adaptive Channel Mixing Graph Isomorphism Network) encoder-decoder architecture with a masked node attribute prediction objective. Please check the associated [preprint](https://arxiv.org/pdf/2603.00143) for details.
+This repository contains the pre-trained model from the paper [GrapHist: Graph Self-Supervised Learning for Histopathology](https://huggingface.co/papers/2603.00143).
+
+Pre-trained on the [graph-tcga-brca](https://huggingface.co/datasets/ogutsevda/graph-tcga-brca) dataset, it employs an **ACM-GIN** (Adaptive Channel Mixing Graph Isomorphism Network) encoder-decoder architecture with a masked node attribute prediction objective.
+
+- **Paper:** [arXiv:2603.00143](https://arxiv.org/abs/2603.00143)
+- **Code:** [GitHub Repository](https://github.com/ogutsevda/graphist)
 
 <p align="center">
   <img src="graphist.png" alt="GrapHist architecture" width="100%">
@@ -35,10 +41,8 @@ graphist/
 
 ## Requirements
 
-```
-torch
-torch-geometric
-huggingface_hub
+```bash
+pip install torch torch-geometric huggingface_hub
 ```
 
 The model expects graphs in PyTorch Geometric format with `x`, `edge_index`, `edge_attr`, and `batch`.