论文标题
关于node2vec的令人惊讶的行为
On the Surprising Behaviour of node2vec
论文作者
论文摘要
图形嵌入技术是现代图学习研究的主要内容。当使用嵌入式进行下游任务（例如分类）时，有关其稳定性和鲁棒性的信息，即它们对噪声，随机效应或特定参数选择的敏感性变得越来越重要。作为嵌入方案最突出的图形之一，我们专注于Node2VEC，并从多个角度分析其嵌入质量。我们的发现表明，在参数选择方面，嵌入质量是不稳定的，我们提出了在实践中纠正这种情况的策略。
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.
