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

Rik Sarkar; Benedek Rózemberczki

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

Rik Sarkar, Benedek Rózemberczki

Research output: Contribution to conference › Paper › peer-review

Abstract

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 forassessing the predictive performance of novel proximity preserving and structural role-based node embedding algorithms.

Original language	English
Number of pages	5
Publication status	Published - 16 Apr 2021
Event	Workshop on Graph Learning Benchmarks @TheWebConf 2021 - Online Duration: 16 Apr 2021 → 16 Apr 2021 https://graph-learning-benchmarks.github.io/

Conference

Conference	Workshop on Graph Learning Benchmarks @TheWebConf 2021
Abbreviated title	GLB 2021
Period	16/04/21 → 16/04/21
Internet address	https://graph-learning-benchmarks.github.io/

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@conference{e084802619a04a5e97d696e2637fac39,

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

abstract = "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 forassessing the predictive performance of novel proximity preserving and structural role-based node embedding algorithms.",

author = "Rik Sarkar and Benedek R{\'o}zemberczki",

year = "2021",

month = apr,

day = "16",

language = "English",

note = "Workshop on Graph Learning Benchmarks @TheWebConf 2021, GLB 2021 ; Conference date: 16-04-2021 Through 16-04-2021",

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TY - CONF

T1 - Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

AU - Sarkar, Rik

AU - Rózemberczki, Benedek

PY - 2021/4/16

Y1 - 2021/4/16

N2 - 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 forassessing the predictive performance of novel proximity preserving and structural role-based node embedding algorithms.

AB - 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 forassessing the predictive performance of novel proximity preserving and structural role-based node embedding algorithms.

M3 - Paper

T2 - Workshop on Graph Learning Benchmarks @TheWebConf 2021

Y2 - 16 April 2021 through 16 April 2021

ER -

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

Abstract

Conference

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