Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

As large-scale graphs become increasingly more prevalent, it poses significant
computational challenges to process, extract and analyze large graph data. Graph …

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 …

We scrutinise an important observation plaguing scene-level sketch research--that a
significant portion of scene sketches are" partial". A quick pilot study reveals:(i) a scene …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

We establish a bridge between spectral clustering and Gromov-Wasserstein Learning
(GWL), a recent optimal transport-based approach to graph partitioning. This connection …

Can we use machine learning to compress graph data? The absence of ordering in graphs
poses a significant challenge to conventional compression algorithms, limiting their …

Graph comparison deals with identifying similarities and dissimilarities between graphs. A
major obstacle is the unknown alignment of graphs, as well as the lack of accurate and …

A novel method for comparing non-aligned graphs of various sizes is proposed, based on
the Wasserstein distance between graph signal distributions induced by the respective …

Graph kernels have become a standard approach for tackling the graph similarity and
learning tasks at the same time. Most graph kernels proposed so far are instances of the R …