Update README.md

README.md

@@ -20,7 +20,7 @@ size_categories:
 
 **Graph-BRACS** is a graph-level classification dataset derived from the [BRACS (BReAst Carcinoma Subtyping)](https://www.bracs.icar.cnr.it/) histopathology dataset. Each region-of-interest (ROI) image is converted into a **cell-graph** where nodes represent detected cell nuclei and edges encode spatial proximity, enabling graph-based learning for fine-grained breast lesion subtyping across 7 clinically relevant 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.
 
@@ -105,12 +105,12 @@ If you use this dataset, please cite both our work, and the original BRACS 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}, 
 }
 ```