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

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arxiv 2101.03091 v2 pith:W42PGOUK submitted 2021-01-08 cs.SI cs.AIcs.LG

Benedek Rozemberczki , Rik Sarkar This is my paper · ORCID
classification cs.SIcs.AIcs.LG
keywords nodegamerspreservingproximityrole-basedstructuraltwitchdataset
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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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Forward citations

Cited by 4 Pith papers

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