Proximity preserving and structural role-based node embeddings became a prime workhorse of applied graph mining. Novel node embedding techniques are repetitively tested on the same benchmark datasets which led to a range of methods with questionable performance gains . In this paper, we propose Twitch Gamers a new social network dataset  with multiple potential target attributes. Our descriptive analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity-preserving and structural role-based node embedding algorithms.
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining. Novel node embedding techniques are often tested on a restricted set of benchmark datasets . In this paper, we propose a new diverse social network dataset called Twitch Gamers with multiple potential target attributes. Our analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity preserving and structural role-based node embedding algorithms.
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became
have become
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repetitively tested on the same benchmark datasets which led to a range of methods with questionable performance gains
often tested on a restricted set of benchmark datasets
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Twitch Gamers a new
a new diverse
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called Twitch Gamers
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descriptive
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proximity-preserving
proximity preserving