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

arXiv: https://arxiv.org/abs/2101.03091 · HTML: https://mlsanity.com/paper/2101.03091/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Multi-scale Attributed Node Embedding](https://mlsanity.com/paper/1909.13021/) (2019)
- [Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://mlsanity.com/paper/1710.02971/) (2017)
- [Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models](https://mlsanity.com/paper/2005.07959/) (2020)
- [Learning Structural Node Embeddings Via Diffusion Wavelets](https://mlsanity.com/paper/1710.10321/) (2017)
- [GEMSEC: Graph Embedding with Self Clustering](https://mlsanity.com/paper/1802.03997/) (2018)
- [Attributed Social Network Embedding](https://mlsanity.com/paper/1705.04969/) (2017)
- [Billion-scale Network Embedding with Iterative Random Projection](https://mlsanity.com/paper/1805.02396/) (2018)
- [Little Ball of Fur: A Python Library for Graph Sampling](https://mlsanity.com/paper/2006.04311/) (2020)
- [Learning Role-based Graph Embeddings](https://mlsanity.com/paper/1802.02896/) (2018)
- [Fast Sequence-Based Embedding with Diffusion Graphs](https://mlsanity.com/paper/2001.07463/) (2020)

## Built on by (most cited first, 30 of 49)

- [Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods](https://mlsanity.com/paper/2110.14446/) (2021)
- [Energy-based Out-of-Distribution Detection for Graph Neural Networks](https://mlsanity.com/paper/2302.02914/) (2023)
- [Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond](https://mlsanity.com/paper/2209.06177/) (2022)
- [SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization](https://mlsanity.com/paper/2303.09778/) (2023)
- [Graph Out-of-Distribution Generalization via Causal Intervention](https://mlsanity.com/paper/2402.11494/) (2024)
- [GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data](https://mlsanity.com/paper/2409.14500/) (2024)
- [Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance](https://mlsanity.com/paper/2110.06893/) (2021)
- [Augmentation-Free Graph Contrastive Learning with Performance Guarantee](https://mlsanity.com/paper/2204.04874/) (2022)
- [GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation](https://mlsanity.com/paper/2502.05780/) (2025)
- [Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph](https://mlsanity.com/paper/2211.10890/) (2022)
- [Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining](https://mlsanity.com/paper/2210.04081/) (2022)
- [BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning](https://mlsanity.com/paper/2211.14568/) (2022)
- [Deformable Graph Transformer](https://mlsanity.com/paper/2206.14337/) (2022)
- [Mixture of Weak & Strong Experts on Graphs](https://mlsanity.com/paper/2311.05185/) (2023)
- [Shape-aware Graph Spectral Learning](https://mlsanity.com/paper/2310.10064/) (2023)
- [Fair Submodular Cover](https://mlsanity.com/paper/2407.04804/) (2024)
- [Provable Filter for Real-world Graph Clustering](https://mlsanity.com/paper/2403.03666/) (2024)
- [Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks](https://mlsanity.com/paper/2212.04537/) (2022)
- [Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling](https://mlsanity.com/paper/2406.10425/) (2024)
- [Higher-Order Causal Message Passing for Experimentation with Complex Interference](https://mlsanity.com/paper/2411.00945/) (2024)
- [Improved Approximations for Hard Graph Problems using Predictions](https://mlsanity.com/paper/2505.23967/) (2025)
- [On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling](https://mlsanity.com/paper/2306.07252/) (2023)
- [Node Injection Link Stealing Attack](https://mlsanity.com/paper/2307.13548/) (2023)
- [Graph Relation Aware Continual Learning](https://mlsanity.com/paper/2308.08259/) (2023)
- [Uplifting Message Passing Neural Network with Graph Original Information](https://mlsanity.com/paper/2210.05382/) (2022)
- [Self-Supervised Node Representation Learning via Node-to-Neighbourhood Alignment](https://mlsanity.com/paper/2302.04626/) (2023)
- [An Empirical Evaluation of Rewiring Approaches in Graph Neural Networks](https://mlsanity.com/paper/2305.19717/) (2023)
- [Learning Divergence Fields for Shift-Robust Graph Representations](https://mlsanity.com/paper/2406.04963/) (2024)
- [An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks](https://mlsanity.com/paper/2505.20074/) (2025)
- [NetEffect: Discovery and Exploitation of Generalized Network Effects](https://mlsanity.com/paper/2301.00270/) (2022)

## Abstract (arXiv)

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.