Datasets

Datasets collected for network science, graph mining, deep learning and general machine learning research.

<p align="center"> <img width="600" src="/images/field.png"> </p>

Contents

1. Twitch Gamers
2. LastFM Asia Social Network
3. Deezer Europe Social Network
4. GitHub StarGazer Graphs
5. Twitch Ego Nets
6. Reddit Thread Graphs
7. Deezer Ego Nets
8. GitHub Social Network
9. Deezer Social Networks
10. Facebook Page-Page Networks
11. Wikipedia Article Networks
12. Twitch Social Networks
13. Facebook Large Page-Page Network

Twitch Gamers

<p align="center"> <img width="200" src="/images/twitch.png"> </p>

Description

<p align="justify"> A social network of Twitch users collected from the public API in Spring 2018. Nodes are Twitch users and edges are mutual follower relationships between them. The graph forms a single strongly connected component without missing attributes. The machine learning tasks related to the graph are count data regression and node classification. There are 6 specific tasks:</p>

• Explicit content streamer identification.
• Broadcaster language prediction.
• User lifetime estimation.
• Churn prediction.
• Affiliate status identification.
• View count estimation.

Links

• Twitch Gamers

Properties

• Directed: No.
• Node features: No.
• Edge features: No.
• Node labels: Yes.
• Temporal: No.

| | Twitch Gamers | |---|---| | Nodes |168,114 | | Edges | 6,797,557 | | Density | 0.0005 | | Transitvity | 0.0184|

Possible tasks

• Binary node classification
• Multi-class node classification
• Count data regression
• Link prediction
• Community detection
• Community detection with ground truth
• Network visualization

Citing

>@misc{rozemberczki2021twitch,
       title = {Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings}, 
       author = {Benedek Rozemberczki and Rik Sarkar},
       year = {2021},
       eprint = {2101.03091},
       archivePrefix = {arXiv},
       primaryClass = {cs.SI}
       }

LastFM Asia Social Network

<p align="center"> <img width="200" src="/images/lastfm.png"> </p>

Description

<p align="justify"> A social network of LastFM users which was collected from the public API in March 2020. Nodes are LastFM users from Asian countries and edges are mutual follower relationships between them. The vertex features are extracted based on the artists liked by the users. The task related to the graph is multinomial node classification - one has to predict the location of users. This target feature was derived from the country field for each user. </p>

Links

• LastFM Asia Social Network

Properties

• Directed: No.
• Node features: Yes.
• Edge features: No.
• Node labels: Yes. Multinomial.
• Temporal: No.

| | LastFM | |---|---| | Nodes |7,624 | | Edges | 27,806 | | Density | 0.001 | | Transitvity | 0.179 |

Possible tasks

• Multi-class node classification
• Link prediction
• Community detection
• Network visualization

Citing

@inproceedings{feather,
               title={{Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models}},
               author={Benedek Rozemberczki and Rik Sarkar},
               year={2020},
	       pages={1325–1334},
	       booktitle={Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},
	       organization={ACM},
}

Deezer Europe Social Network

<p align="center"> <img width="200" src="/images/deezer.jpg"> </p>

Description

<p align="justify"> A social network of Deezer users which was collected from the public API in March 2020. Nodes are Deezer users from European countries and edges are mutual follower relationships between them. The vertex features are extracted based on the artists liked by the users. The task related to the graph is binary node classification - one has to predict the gender of users. This target feature was derived from the name field for each user. </p>

Links

• Deezer Europe Social Network

Properties

• Directed: No.
• Node features: Yes.
• Edge features: No.
• Node labels: Yes. Binary.
• Temporal: No.

| | Deezer | |---|---| | Nodes |28,281 | | Edges | 92,752 | | Density | 0.0002 | | Transitvity | 0.0959 |

Possible tasks

• Binary node classification
• Link prediction
• Community detection
• Network visualization

Citing

@inproceedings{feather,
               title={{Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models}},
               author={Benedek Rozemberczki and Rik Sarkar},
               year={2020},
	       pages={1325–1334},
	       booktitle={Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},
	       organization={ACM},
}

GitHub StarGazer Graphs

<p align="center"> <img width="200" src="/images/Octocat.png"> </p>

Description

<p align="justify"> The social networks of developers who starred popular machine learning and web development repositories (with at least 10 stars) until 2019 August. Nodes are users and links are follower relationships. The task is to decide whether a social network belongs to a web or machine learning repository. We only included the largest component (at least with 10 users) of graphs.</p>

Link

• GitHub StarGazer Graphs + Target

Properties

• Number of graphs: 12,725
• Directed: No.
• Node features: No.
• Edge features: No.
• Graph labels: Yes. Binary-labeled.
• Temporal: No.

| | Min |Max | |---|---|---| | Nodes |10 | 957 | | Density | 0.003 |0.561 | | Diameter | 2 | 18 |

Possible Tasks

• Graph classification

Citing

@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},
}

Twitch Ego Nets

<p align="center"> <img width="200" src="/images/twitch.png"> </p>

Description

<p align="justify"> The ego-nets of Twitch users who participated in the partnership program in April 2018. Nodes are users and links are friendships. The binary classification task is to predict using the ego-net whether the ego user plays a single or multple games. Players who play a single game usually have a more dense ego-net.</p>

Link

• Twitch Ego Nets Part

Properties

• Number of graphs: 127,094
• Directed: No.
• Node features: No.
• Edge features: No.
• Graph labels: Yes. Binary-labeled.
• Temporal: No.

| | Min |Max | |---|---|---| | Nodes |14 | 52 | | Density | 0.038 |0.967 | | Diameter | 1 | 2 |

Possible Tasks

• Graph classification

Citing

@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},
}

Reddit Thread Graphs

<p align="center"> <img width="200" src="/images/reddit-logo-png-transparent.png"> </p>

Description

<p align="justify"> Discussion and non-discussion based threads from Reddit which we collected in May 2018. Nodes are Reddit users who participate in a discussion and links are replies between them. The task is to predict whether a thread is discussion based or not (binary classification). </p>

Link

• Reddit Thread Graphs + Target

Properties

• Number of graphs: 203,088
• Directed: No.
• Node features: No.
• Edge features: No.
• Graph labels: Yes. Binary-labeled.
• Temporal: No.

| | Min |Max | |---|---|---| | Nodes |11 | 97 | | Density | 0.021 |0.382 | | Diameter | 2 | 27 |

Possible Tasks

• Graph classification

Citing

@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},