Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

Abstract

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.