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Epic Games for Proximate Link Regression

Summary

Name: epic-games-plr
Description: This dataset involves a bi-partite review network between game critic companies and games released on Epic Games Store.
Task: The task is predicting the review score given the identity of critic owner company and game, in addition to the time of review and game features.
Date of Creation: 01.07.2024
Last Update: 01.07.2024
Original Source: https://zenodo.org/records/7606569
Contact Information: email
License: CC BY 4.0

Statistics

Category	Data
Number of Nodes	1,156
Number of Edges	17,584
Number of Node Features	573
Number of Edge Features	512
Number of Timestamps	3267

Download

Format: Compressed data.pt which involves a python dictionary as follows:

data = {
  "node_attr": torch.FloatTensor,
  "edge_index": torch.LongTensor,
  "edge_time": torch.FloatTensor,
  "edge_attr": torch.FloatTensor,
  "edge_label": torch.FloatTensor,
  "num_nodes": int
}

Size: 2.57 MB
Location: https://huggingface.co/datasets/ca-aird/epicgames/blob/main/data.zip

Citation

BiBTeX:

@article{,
    title={Benchmarking Edge Regression on Temporal Networks},
    author={Muberra Ozmen and Florence Regol and Thomas Markovich},
    journal={X},
    volume={X},
    number={X},
    pages={X},
    year={X},
    publisher={X}
}

Preprocessing

Epic Games Store is a digital video game storefront. The original dataset contains information on the games released on the platform and their critics provided by different resources. Relevant to this work, the dataset includes two types of records: game and critic.

The critic records are used to define the graph such that the source and destination of the critiques, i.e., the authors' companies and game identities, form the set of vertices and each critic denotes a temporal edge between them. The raw data fields of a critic record, their descriptions and usages are as follows:

Field	Description	Usage
`company`	Company name that rated the game: generated an identification number that is different from any value in `game_id` for each sample in the set of unique values.	Used as source node
`author`	Author commented about the game: not used because of missing values.	Not used
`game_id`	Identification of game.	Used as destination node
`date`	Date of critic: converted to timestamp.	Used as edge time
`rating`	Rating of game (out of 100): normalized to [0, 1].	Used as edge target
`comment`	Author comment about the game: not used because observed after `date`.	Not used
`top_critic`	Verify if is a top critic (authors with verdict): not used because observed after `date`.	Not used

Each vertex, i.e., an author company or game, is associated with a feature vector. The features of game vertices are calculated by textual data such as game description, nominal data such as genres, and interval data such as price based on game records:

Field	Description	Usage
`id`	Identification of game.
`name`	Name of game.
`game_slug`	Short name of game.
`description`	Description of game: concatenated with `name` and `game_slug`, and vectorized to TF-IDF features with a vocabulary size of 512 and a maximum word frequency of 0.8.	Used as node feature
`price`	Price of game: normalized to [0, 1].	Used as node feature
`platform`	Platforms that the game is available on: converted to categorical data with 0/1 indicator.	Used as node feature
`genres`	Genres of game: converted to categorical data with 0/1 indicator.	Used as node feature
`release_date`	Release date of game: converted to timestamp.	Used as node feature
`developer`	Company that developed the game.	Unused
`publisher`	Company that published the game.	Unused

Notes

Acknowledgements:
- All funding was provided by Block Inc.
References:
- Gomes, 2014. Dataset on Epic Games Store.

Author Statement

I, Muberra Ozmen, declare that I bear full responsibility for the dataset described herein, including its contents and compliance with applicable laws and regulations. By providing access to this dataset, I confirm that it is released under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following conditions:

Attribution — appropriate credit must be given to the associated publication indicating if any changes were made. This information should be provided in a manner that is reasonable given the medium, means, and context in which the dataset is shared.

For any use or redistribution of the dataset not permitted under this license, explicit permission from the dataset's creator is required.

The dataset will be hosted on a secure platform that ensures continuous access to the data. We have chosen Hugging Face for its robust infrastructure and capability to handle large datasets. Access to the dataset will be facilitated through a curated interface, providing users with efficient search and retrieval functionalities.

Licensing: The dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to freely use, share, and adapt the material, provided appropriate credit is given to the dataset's creator.

Maintenance: Regular maintenance of the dataset and its hosting platform will be conducted to ensure data integrity, security, and accessibility. Updates to the dataset, if any, will be promptly integrated into the platform to reflect the most current information available.