Erfan
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metadata
license: cc-by-nc-4.0
task_categories:
  - graph-ml
size_categories:
  - 100K<n<1M
configs:
  - config_name: main_data
    data_files: myket.csv
  - config_name: package_name_features
    data_files: app_info.csv

Myket Android Application Install Dataset

This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions.

Data Creation

The dataset was initially generated by the Myket data team, and later cleaned and subsampled by Erfan Loghmani a master student at Sharif University of Technology at the time. The data team focused on a two-week period and randomly sampled 1/3 of the users with interactions during that period. They then selected install and update interactions for three months before and after the two-week period, resulting in interactions spanning about 6 months and two weeks.

We further subsampled and cleaned the data to focus on application download interactions. We identified the top 8000 most installed applications and selected interactions related to them. We retained users with more than 32 interactions, resulting in 280,391 users. From this group, we randomly selected 10,000 users, and the data was filtered to include only interactions for these users. The detailed procedure can be found in here.

Data Structure

The dataset has two main files.

  • myket.csv: This file contains the interaction information and follows the same format as the datasets used in the "JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks" (ACM SIGKDD 2019) project. However, this data does not contain state labels and interaction features, resulting in associated columns being all zero.
  • app_info_sample.csv: This file comprises features associated with applications present in the sample. For each individual application, information such as the approximate number of installs, average rating, count of ratings, and category are included. These features provide insights into the applications present in the dataset. Note: The number of installs, average rating, and the count of ratings info are gathered during the period of interaction data collection, so they may cause information leakage if used for interaction prediction tasks.

Dataset Details

  • Total Instances: 694,121 install interaction instances
  • Instances Format: Triplets of user_id, app_name, timestamp
  • 10,000 users and 7,988 android applications

For a detailed summary of the data's statistics, including information on users, applications, and interactions, please refer to the Python notebook available at summary-stats.ipynb. The notebook provides an overview of the dataset's characteristics and can be helpful for understanding the data's structure before using it for research or analysis.

Top 20 Most Installed Applications

Package Name Count of Interactions
com.instagram.android 15292
ir.resaneh1.iptv 12143
com.tencent.ig 7919
com.ForgeGames.SpecialForcesGroup2 7797
ir.nomogame.ClutchGame 6193
com.dts.freefireth 6041
com.whatsapp 5876
com.supercell.clashofclans 5817
com.mojang.minecraftpe 5649
com.lenovo.anyshare.gps 5076
ir.medu.shad 4673
com.firsttouchgames.dls3 4641
com.activision.callofduty.shooter 4357
com.tencent.iglite 4126
com.aparat 3598
com.kiloo.subwaysurf 3135
com.supercell.clashroyale 2793
co.palang.QuizOfKings 2589
com.nazdika.app 2436
com.digikala 2413

Comparison with SNAP Datasets

The Myket dataset introduced in this repository exhibits distinct characteristics compared to the real-world datasets used by the project. The table below provides a comparative overview of the key dataset characteristics:

Dataset #Users #Items #Interactions Average Interactions per User Average Unique Items per User
Myket 10,000 7,988 694,121 69.4 54.6
LastFM 980 1,000 1,293,103 1,319.5 158.2
Reddit 10,000 984 672,447 67.2 7.9
Wikipedia 8,227 1,000 157,474 19.1 2.2
MOOC 7,047 97 411,749 58.4 25.3

The Myket dataset stands out by having an ample number of both users and items, highlighting its relevance for real-world, large-scale applications. Unlike LastFM, Reddit, and Wikipedia datasets, where users exhibit repetitive item interactions, the Myket dataset contains a comparatively lower amount of repetitive interactions. This unique characteristic reflects the diverse nature of user behaviors in the Android application market environment.

Citation

If you use this dataset in your research, please cite the following preprint:

@misc{loghmani2023effect,
      title={Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks},
      author={Erfan Loghmani and MohammadAmin Fazli},
      year={2023},
      eprint={2308.06862},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}