Datasets collected for network science and machine learning research.
- GitHub Social Network
- Deezer Social Networks
- Facebook Page-Page Networks
- Wikipedia Article Networks
- Twitch Social Networks
- Facebook Large Page-Page Network
A large social network of GitHub developers which was collected from the public API in June 2019. Nodes are developers who have starred at least 10 repositories and edges are mutual follower relationships between them. The vertex features are extracted based on the location, repositories starred, employer and e-mail address. The task related to the graph is binary node classification - one has to predict whether the GitHub user is a web or a machine learning developer. This target feature was derived from the job title of each user.
- Directed: No.
- Node features: Yes.
- Edge features: No.
- Node labels: Yes. Binary-labeled.
- Temporal: No.
| GitHub | |
|---|---|
| Nodes | 37,700 |
| Edges | 289,003 |
| Density | 0.001 |
| Transitvity | 0.013 |
- Binary node classification
- Link prediction
- Community detection
- Network visualization
@misc{rozemberczki2019multiscale,
title = {Multi-scale Attributed Node Embedding},
author = {Benedek Rozemberczki and Carl Allen and Rik Sarkar},
year = {2019},
eprint = {1909.13021},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
The data was collected from the music streaming service Deezer (November 2017). These datasets represent friendship networks of users from 3 European countries. Nodes represent the users and edges are the mutual friendships. We reindexed the nodes in order to achieve a certain level of anonimity. The csv files contain the edges - nodes are indexed from 0. The json files contain the genre preferences of users - each key is a user id, the genres loved are given as lists. Genre notations are consistent across users. In each dataset users could like 84 distinct genres. Liked genre lists were compiled based on the liked song lists. The countries included are Romania, Croatia and Hungary. For each dataset we listed the number of nodes an edges.
- Directed: No.
- Node features: No.
- Edge features: No.
- Node labels: Yes. Multi-labeled.
- Temporal: No.
| RO | HR | HU | |
|---|---|---|---|
| Nodes | 41,773 | 54,573 | 47,538 |
| Edges | 125,826 | 498,202 | 222,887 |
| Density | 0.0001 | 0.0004 | 0.0002 |
| Transitvity | 0.0752 | 0.1146 | 0.0929 |
- Node classification
- Link prediction
- Community detection
- Network visualization
If you find these datasets useful in your research, please cite the following paper:
@inproceedings{rozemberczki2019gemsec,
title={GEMSEC: Graph Embedding with Self Clustering},
author={Rozemberczki, Benedek and Davies, Ryan and Sarkar, Rik and Sutton, Charles},
booktitle={Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2019},
pages={65-72},
year={2019},
organization={ACM}
}
We collected data about Facebook pages (November 2017). These datasets represent blue verified Facebook page networks of different categories. Nodes represent the pages and edges are mutual likes among them. The csv files contain the edges - nodes are indexed from 0. We included 8 different distinct types of pages. These are listed below. For each dataset we listed the number of nodes an edges.
- Directed: No.
- Node features: No.
- Edge features: No.
- Node labels: No.
- Temporal: No.
| Nodes | Edges | Density | Transitvity | |
|---|---|---|---|---|
| Politicians | 5,908 | 41,729 | 0.0024 | 0.3011 |
| Companies | 14,113 | 52,310 | 0.0005 | 0.1532 |
| Athletes | 13,866 | 86,858 | 0.0009 | 0.1292 |
| News Sites | 27,917 | 206,259 | 0.0005 | 0.1140 |
| Public Figures | 11,565 | 67,114 | 0.0010 | 0.1666 |
| Artists | 50,515 | 819,306 | 0.0006 | 0.1140 |
| Government | 7,057 | 89,455 | 0.0036 | 0.2238 |
| TV Shows | 3,892 | 17,262 | 0.0023 | 0.5906 |
- Link prediction
- Community detection
- Network visualization
If you find these datasets useful in your research, please cite the following paper:
@inproceedings{rozemberczki2019gemsec,
title={GEMSEC: Graph Embedding with Self Clustering},
author={Rozemberczki, Benedek and Davies, Ryan and Sarkar, Rik and Sutton, Charles},
booktitle={Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2019},
pages={65-72},
year={2019},
organization={ACM}
}
The data was collected from the English Wikipedia (December 2018). These datasets represent page-page networks on specific topics (chameleons, crocodiles and squirrels). Nodes represent articles and edges are mutual links between them. The edges csv files contain the edges - nodes are indexed from 0. The features json files contain the features of articles - each key is a page id, and node features are given as lists. The presence of a feature in the feature list means that an informative noun appeared in the text of the Wikipedia article. The target csv contains the node identifiers and the average monthly traffic between October 2017 and November 2018 for each page. For each page-page network we listed the number of nodes an edges with some other descriptive statistics.
- Directed: No.
- Node features: Yes.
- Edge features: No.
- Node labels: Yes. Continuous target.
- Temporal: No.
| Chameleon | Crocodile | Squirrel | |
|---|---|---|---|
| Nodes | 2,277 | 11,631 | 5,201 |
| Edges | 31,421 | 170,918 | 198,493 |
| Density | 0.012 | 0.003 | 0.015 |
| Transitvity | 0.314 | 0.026 | 0.348 |
- Regression
- Link prediction
- Community detection
- Network visualization
If you find these datasets useful in your research, please cite the following paper:
@misc{rozemberczki2019multiscale,
title = {Multi-scale Attributed Node Embedding},
author = {Benedek Rozemberczki and Carl Allen and Rik Sarkar},
year = {2019},
eprint = {1909.13021},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
These datasets used for node classification and transfer learning are Twitch user-user networks of gamers who stream in a certain language. Nodes are the users themselves and the links are mutual friendships between them. Vertex features are extracted based on the games played and liked, location and streaming habits. Datasets share the same set of node features, this makes transfer learning across networks possible. These social networks were collected in May 2018. The supervised task related to these networks is binary node classification - one has to predict whether a streamer uses explicit language.
| DE | EN | ES | FR | PT | RU | |
|---|---|---|---|---|---|---|
| Nodes | 9,498 | 7,126 | 4,648 | 6,549 | 1,912 | 4,385 |
| Edges | 153,138 | 35,324 | 59,382 | 112,666 | 31,299 | 37,304 |
| Density | 0.003 | 0.002 | 0.006 | 0.005 | 0.017 | 0.004 |
| Transitvity | 0.047 | 0.042 | 0.084 | 0.054 | 0.131 | 0.049 |
- Binary node classification
- Link prediction
- Community detection
- Network visualization
@misc{rozemberczki2019multiscale,
title = {Multi-scale Attributed Node Embedding},
author = {Benedek Rozemberczki and Carl Allen and Rik Sarkar},
year = {2019},
eprint = {1909.13021},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
This webgraph is a page-page graph of verified Facebook sites. Nodes represent official Facebook pages while the links are mutual likes between sites. Node features are extracted from the site descriptions that the page owners created to summarize the purpose of the site. This graph was collected through the Facebook Graph API in November 2017 and restricted to pages from 4 categories which are defined by Facebook. These categories are: politicians, governmental organizations, television shows and companies. The task related to this dataset is multi-class node classification for the 4 site categories.
- Directed: No.
- Node features: Yes.
- Edge features: No.
- Node labels: Yes. Binary-labeled.
- Temporal: No.
| Nodes | 22,470 |
| Edges | 171,002 |
| Density | 0.001 |
| Transitvity | 0.232 |
- Multi-class node classification
- Link prediction
- Community detection
- Network visualization
@misc{rozemberczki2019multiscale,
title = {Multi-scale Attributed Node Embedding},
author = {Benedek Rozemberczki and Carl Allen and Rik Sarkar},
year = {2019},
eprint = {1909.13021},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}