lastfm_asia — LastFM Asia mutual followers (2020)
- Description
A social network of users of the music website Last.fm from Asian countries, collected from its public API in March 2020 by B. Rozemberczki and R. Sarkar (FEATHER study) for benchmarks of node classification methods. Nodes are users, and an undirected edge connects two users who follow each other (a mutual follower relationship). The user names were replaced by consecutive indices. Each user has a list of binary features extracted from the artists the user liked (only the indices of the features are given, not the artists; 173 users have no features), and a country label, derived from the country field of the user profile, which is the target of the classification task proposed with the data. The countries are given only as indices from 0 to 17, without their names.
This network is unrelated to the other Last.fm entries (
lastfm,lastfm_aminer,lastfm_finland,lastfm_friendships), which were collected at other times and with other selections of users.The network has the following properties:
country: the index of the user's country (0 to 17; the names are not given).features: the indices of the user's binary (artist) features.
- Tags
- Social Online Unweighted Metadata
- Citation
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- B. Rozemberczki and R. Sarkar, "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models." Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM), 1325-1334 (2020). 10.1145/3340531.3411866 Sci-Hub
- Upstream URL OK
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https://snap.stanford.edu/data/feather-lastfm-social.html
https://github.com/benedekrozemberczki/datasets - Upstream license
- MIT License, https://github.com/benedekrozemberczki/datasets/blob/3b19135105432cbca65765c1113b91c395ed9d44/LICENSE
- 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
- Ridiculograms*
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