Wasserstein Embedding for Graph Learning
This addresses the computational bottleneck in graph similarity methods for researchers and practitioners in graph machine learning, though it appears incremental as it builds on existing Wasserstein distance concepts.
The paper tackles the problem of embedding entire graphs for graph-level prediction tasks by proposing Wasserstein Embedding for Graph Learning (WEGL), which uses Wasserstein distance to measure similarity between node embedding distributions and reduces computational complexity from quadratic to linear, achieving state-of-the-art classification performance on benchmark tasks.
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.