论文标题
随机性对节点嵌入稳定性的影响
The Effects of Randomness on the Stability of Node Embeddings
论文作者
论文摘要
我们系统地评估了由于随机性，即在给定相同的算法和图形给定结果的随机变化而导致的最新节点嵌入算法的稳定性。我们应用五种节点嵌入式算法---希望，线，node2vec，sdne和图形---用于合成和经验图，并评估其在随机性下相对于（i）嵌入空间的几何形状以及（ii）在下游任务中的性能。我们发现在嵌入空间的几何形状中，与节点的中心性无关。在评估下游任务时，我们发现节点分类的准确性似乎不受随机播种的影响，而节点的实际分类可能会发生很大变化。这表明使用节点嵌入时需要考虑不稳定效应。我们的工作与对节点嵌入方法的有效性，可靠性和可重复性感兴趣的研究人员和工程师有关。
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorithms and graphs. We apply five node embeddings algorithms---HOPE, LINE, node2vec, SDNE, and GraphSAGE---to synthetic and empirical graphs and assess their stability under randomness with respect to (i) the geometry of embedding spaces as well as (ii) their performance in downstream tasks. We find significant instabilities in the geometry of embedding spaces independent of the centrality of a node. In the evaluation of downstream tasks, we find that the accuracy of node classification seems to be unaffected by random seeding while the actual classification of nodes can vary significantly. This suggests that instability effects need to be taken into account when working with node embeddings. Our work is relevant for researchers and engineers interested in the effectiveness, reliability, and reproducibility of node embedding approaches.
