Dictionary learning, which approximates data samples by a set of shared atoms, is a
fundamental task in representation learning. However, dictionary learning over graphs …

Network alignment is a critical steppingstone behind a variety of multi-network mining tasks.
Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based …

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

A well-trained deep neural network on balanced datasets usually exhibits the Neural
Collapse (NC) phenomenon, which is an informative indicator of the model achieving good …

Multimodal hate detection, which aims to identify the harmful content online such as memes,
is crucial for building a wholesome internet environment. Previous work has made …

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 …

Finding the mean of sampled data is a fundamental task in machine learning and statistics.
However, in cases where the data samples are graph objects, defining a mean is an …

Estimating treatment effects from observational data suffers from the issue of confounding
bias, which is induced by the imbalanced confounder distributions between the treated and …

Distance measures between graphs are important primitives for a variety of learning tasks. In
this work, we describe an unsupervised, optimal transport based approach to define a …

We study the problem of network regression, where one is interested in how the topology of
a network changes as a function of Euclidean covariates. We build upon recent …