Update README.md

README.md

@@ -20,7 +20,7 @@ size_categories:
 
 **Graph-NuCLS** is a node-level classification dataset derived from the [NuCLS](https://sites.google.com/view/nucls/home) dataset with "main" labels. Each tissue patch is converted into a **cell-graph** where nodes represent detected cell nuclei and edges encode spatial proximity. The task is predicting the cell type of each nucleus across 7 classes. Note that node features describe cell morphology, texture, and color intensity whereas edge features are Euclidean distance in micrometers.
 
-This dataset is part of the paper **GrapHist: Graph Self-Supervised Learning for Histopathology**.
+This dataset is part of the paper [GrapHist: Graph Self-Supervised Learning for Histopathology](https://arxiv.org/pdf/2603.00143).
 
 > ⚠️ **Edge Weight Note**: While the architecture in GrapHist supports both positive and negative edge weights, by default edge features represent Euclidean distances—meaning farther nodes have larger, positive values. This can be counterintuitive for many graph neural network models. We recommend experimenting with edge weights, such as using their inverse (e.g., `1/distance`) or negative distance (e.g., `-distance`), to better capture proximity and benefit learning.
 
@@ -111,12 +111,12 @@ If you use this dataset, please cite both our work, and the original NuCLS datas
 
 **GrapHist (this dataset):**
 ```bibtex
-@article{graphist2025,
-  title     = {GrapHist: Graph Self-Supervised Learning for Histopathology},
-  author    = {TODO},
-  journal   = {TODO},
-  year      = {TODO},
-  note      = {TODO: add full citation}
+@misc{ogut2026graphist,
+    title={GrapHist: Graph Self-Supervised Learning for Histopathology}, 
+    author={Sevda Öğüt and Cédric Vincent-Cuaz and Natalia Dubljevic and Carlos Hurtado and Vaishnavi Subramanian and Pascal Frossard and Dorina Thanou},
+    year={2026},
+    eprint={2603.00143},
+    url={https://arxiv.org/abs/2603.00143}, 
 }
 ```