On the Surprising Behaviour of node2vec
| Autor: | Hacker, Celia, Rieck, Bastian |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Graph embedding techniques are a staple of modern graph learning research. When using embeddings for downstream tasks such as classification, information about their stability and robustness, i.e., their susceptibility to sources of noise, stochastic effects, or specific parameter choices, becomes increasingly important. As one of the most prominent graph embedding schemes, we focus on node2vec and analyse its embedding quality from multiple perspectives. Our findings indicate that embedding quality is unstable with respect to parameter choices, and we propose strategies to remedy this in practice. Comment: ICML 2022 Workshop on Topology, Algebra, and Geometry in Machine Learning (Camera-Ready Version) |
| Databáze: | arXiv |
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