thgl_myket — Myket app market installations and updates (thgl-myket, TGB 2.0, 2020-2021)
- Description
A temporal bipartite network of the users of the Myket Android app market (an Iranian app store) and the apps they installed or updated, from June 2020 to January 2021, from the
thgl-myketdataset of the Temporal Graph Benchmark (TGB 2.0), provided by the Myket Corporation. Each timestamped directed edge from a user to an app is an installation of the app (for the first time) or an update of it. The users and apps are anonymized (they are identified only by indices). A smaller, downsampled version of the data was published earlier by Loghmani and Fazli (2023); this one has all the data.The network has the following properties:
is_app: whether the node is an app (otherwise a user).- Edge
update: whether the event is an update of the app (otherwise a first installation). - Edge
time: the time of the event, in Unix time, as given by TGB (the times span from 2020-06-18 03:55 to 2021-01-01 04:59 UTC, so that they may be shifted by the offset of a time zone).
- Tags
- Economic Commerce Online Unweighted Multigraph Timestamps Metadata
- Citation
-
- J. Gastinger, S. Huang, M. Galkin, E. Loghmani, A. Parviz, F. Poursafaei, J. Danovitch, E. Rossi, I. Koutis, H. Stuckenschmidt, R. Rabbany, G. Rabusseau, "TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs." Advances in Neural Information Processing Systems 37 (Datasets and Benchmarks Track) (2024) 10.48550/arXiv.2406.09639 Sci-Hub
- E. Loghmani, M. Fazli, "Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks." arXiv:2308.06862 (2023) 10.48550/arXiv.2308.06862 Sci-Hub
- Upstream URL OK
-
https://tgb.complexdatalab.com/docs/thg/
https://github.com/shenyangHuang/TGB - Upstream license
- CC BY-NC 4.0, https://creativecommons.org/licenses/by-nc/4.0/
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties