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--- |
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license: gpl-3.0 |
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task_categories: |
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- graph-ml |
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--- |
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# Dataset Card for Twitch ego nets |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [External Use](#external-use) |
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- [PyGeometric](#pygeometric) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Properties](#data-properties) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **[Homepage](https://snap.stanford.edu/data/twitch_ego_nets.html)** |
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- **Paper:**: (see citation) |
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### Dataset Summary |
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The `Twitch ego nets` dataset contains ' ego-nets of Twitch users who participated in the partnership program in April 2018. Nodes are users and links are friendships.' (doc). |
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### Supported Tasks and Leaderboards |
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The related task is the binary classification to predict whether a user plays a single or multple games. |
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## External Use |
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### PyGeometric |
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To load in PyGeometric, do the following: |
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```python |
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from datasets import load_dataset |
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from torch_geometric.data import Data |
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from torch_geometric.loader import DataLoader |
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dataset_hf = load_dataset("graphs-datasets/<mydataset>") |
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# For the train set (replace by valid or test as needed) |
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dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] |
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dataset_pg = DataLoader(dataset_pg_list) |
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``` |
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## Dataset Structure |
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### Dataset information |
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- 127,094 graphs |
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### Data Fields |
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Each row of a given file is a graph, with: |
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- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges |
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- `y` (list: #labels): contains the number of labels available to predict |
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- `num_nodes` (int): number of nodes of the graph |
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### Data Splits |
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This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset. |
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## Additional Information |
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### Licensing Information |
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The dataset has been released under GPL-3.0 license. |
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### Citation Information |
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See also [github](https://github.com/benedekrozemberczki/karateclub). |
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``` |
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@inproceedings{karateclub, |
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title = {{Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs}}, |
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author = {Benedek Rozemberczki and Oliver Kiss and Rik Sarkar}, |
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year = {2020}, |
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pages = {3125–3132}, |
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booktitle = {Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)}, |
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organization = {ACM}, |
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} |
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``` |
