论文标题
Wasserstein嵌入图形学习
Wasserstein Embedding for Graph Learning
论文作者
论文摘要
我们提出了用于图形学习的Wasserstein嵌入（WEGL），这是一个新颖而快速的框架，用于将整个图嵌入矢量空间中，其中各种机器学习模型适用于图形级别的预测任务。我们利用新的见解来定义图形之间的相似性，这是其节点嵌入分布之间相似性的函数。具体而言，我们使用Wasserstein距离来测量不同图的节点嵌入之间的差异。与先前的工作不同，我们避免对图之间的距离进行成对计算，并降低图表数中从二次到线性的计算复杂性。 WEGL计算从参考分布到每个节点嵌入的Monge图，并基于这些图创建图形的固定尺寸向量表示。我们在各种基准的图形 - 构图预测任务上评估了新的图形嵌入方法，显示了最新的分类性能，同时具有出色的计算效率。该代码可在https://github.com/navid-naderi/wegl上找到。
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks. We leverage new insights on defining similarity between graphs as a function of the similarity between their node embedding distributions. Specifically, we use the Wasserstein distance to measure the dissimilarity between node embeddings of different graphs. Unlike prior work, we avoid pairwise calculation of distances between graphs and reduce the computational complexity from quadratic to linear in the number of graphs. WEGL calculates Monge maps from a reference distribution to each node embedding and, based on these maps, creates a fixed-sized vector representation of the graph. We evaluate our new graph embedding approach on various benchmark graph-property prediction tasks, showing state-of-the-art classification performance while having superior computational efficiency. The code is available at https://github.com/navid-naderi/WEGL.
