Update README.md

README.md

@@ -1,20 +1,80 @@
 ---
-dataset_info:
-  features:
-  - name: edge_index
-    sequence:
-      sequence: int64
-  - name: y
-    sequence: int64
-  - name: num_nodes
-    dtype: int64
-  splits:
-  - name: full
-    num_bytes: 20413824
-    num_examples: 9629
-  download_size: 1729018
-  dataset_size: 20413824
+licence: unknown
+license: gpl-3.0
 ---
-# Dataset Card for "deezer_ego_nets_small"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Dataset Card for Deezer ego nets
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+- [External Use](#external-use)
+  - [PyGeometric](#pygeometric)
+- [Dataset Structure](#dataset-structure)
+  - [Data Properties](#data-properties)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+- **[Homepage](https://snap.stanford.edu/data/deezer_ego_nets.html)**
+- **Paper:**:  (see citation)
+
+
+### Dataset Summary
+The Deezer ego nets dataset contains ego-nets of Eastern European users collected from the music streaming service Deezer in February 2020. Nodes are users and edges are mutual follower relationships. 
+
+### Supported Tasks and Leaderboards
+The related task is the binary classification to predict gender for the ego node in the graph.
+
+## External Use
+### PyGeometric
+To load in PyGeometric, do the following:
+
+```python
+from datasets import load_dataset
+
+from torch_geometric.data import Data
+from torch_geometric.loader import DataLoader
+
+dataset_hf = load_dataset("graphs-datasets/<mydataset>")
+# For the train set (replace by valid or test as needed)
+dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
+dataset_pg = DataLoader(dataset_pg_list)
+```
+
+## Dataset Structure
+### Data Fields
+
+Each row of a given file is a graph, with: 
+- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
+- `y` (list:  #labels): contains the number of labels available to predict
+- `num_nodes` (int): number of nodes of the graph
+
+### Data Splits
+
+This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
+
+## Additional Information
+
+### Licensing Information
+The dataset has been released under GPL-3.0 license.
+
+### Citation Information
+See also [github](https://github.com/benedekrozemberczki/karateclub).
+
+```
+ @inproceedings{karateclub,
+    title = {{Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs}},
+    author = {Benedek Rozemberczki and Oliver Kiss and Rik Sarkar},
+    year = {2020},
+    pages = {3125–3132},
+    booktitle = {Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},
+    organization = {ACM},
+}
+```