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Twitch Social Networks (6 languages)

Task: Explicit-content streamer classification
Size band: small-medium · Label type: semantic
Label column: mature · Converter: converters/convert_musae.py

Six independent friendship networks of Twitch streamers (DE, ENGB, ES, FR, PTBR, RU), 1.9k-9.5k nodes each. The binary label flags streamers who use explicit language. Six graphs from one source enable cross-graph generalization studies (train on one language, test on another).

Converted graphs (neext/)

graph nodes edges classes feature cols isolated class counts
de 9,498 153,138 2 0 0 1: 5,742, 0: 3,756
engb 7,126 35,324 2 0 0 1: 3,888, 0: 3,238
es 4,648 59,382 2 0 0 0: 3,288, 1: 1,360
fr 6,549 112,666 2 0 0 0: 4,135, 1: 2,414
ptbr 1,912 31,299 2 0 0 0: 1,251, 1: 661
ru 4,385 37,304 2 0 0 0: 3,310, 1: 1,075

Conversion notes: Six independent language graphs; 'new_id' is the graph node id; label = mature (explicit-content flag, 0/1). Feature JSONs (games/habits token lists) not columnized.

Source

  • twitch.zip — 2,842,994 bytes, sha256 65a6c4c23da23889…, fetched 2026-07-23

License: SNAP / MUSAE; GPL-3.0 code, cite MUSAE
Citation: Rozemberczki, Allen, Sarkar. Multi-Scale Attributed Node Embedding (MUSAE). Journal of Complex Networks 2021.

Caveats

  • Feature JSONs (games/habits token lists) not columnized; FR listed two accounts twice (deduped, labels agreed).

Generated by converters/make_cards.py; stats from metadata.json. Raw files: source/. NEExT tables: neext/.

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