graphs-datasets/twitch_egos

---
license: gpl-3.0
task_categories:
- graph-ml
---

# Dataset Card for Twitch 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/twitch_ego_nets.html)**
- **Paper:**: (see citation)

### Dataset Summary
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).

### Supported Tasks and Leaderboards
The related task is the binary classification to predict whether a user plays a single or multple games.

## 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
### Dataset information
- 127,094 graphs

### 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},
}
```

原始信息汇总

数据集概述

数据集描述

• 名称: Twitch ego nets
• 概述: 该数据集包含2018年4月参与Twitch合作伙伴计划的用户的自我网络。节点代表用户，链接代表友谊。
• 任务: 二元分类任务，预测用户是否玩单一或多款游戏。

数据集结构

• 图数量: 127,094个图
• 数据字段:
  • edge_index (列表: 2 x #edges): 构成边的节点对
  • y (列表: #labels): 可用预测的标签数量
  • num_nodes (整数): 图中的节点数量
• 数据分割: 数据未分割，建议使用交叉验证。

附加信息

• 许可证: GPL-3.0
• 引用信息: 参见Karate Club的引用信息。