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

Proximity preserving and structural role-based node embeddings have become a\nprime workhorse of applied graph mining. Novel node embedding techniques are\noften tested on a restricted set of benchmark datasets. In this paper, we\npropose a new diverse social network dataset called Twitch Gamers with multiple\npotential target attributes. Our analysis of the social network and node\nclassification experiments illustrate that Twitch Gamers is suitable for\nassessing the predictive performance of novel proximity preserving and\nstructural role-based node embedding algorithms.\n