Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and
representation learning. However, GNNs tend to suffer from over-smoothing problems and …

Hyperbolic space embeddings have been shown beneficial for many learning tasks where
data have an underlying hierarchical structure. Consequently, many machine learning tools …

With the proliferation of online social networks, a great deal of online user action data has
been generated. Such data has enabled the study of information diffusion prediction, which …

Topological features based on persistent homology capture high-order structural information
so as to augment graph neural network methods. However, computing extended persistent …

Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine
learning tasks that spans from manifold learning to graph classification. A pivotal technique …

Complex systems can be more accurately described by higher-order interactions among
multiple units. Hypergraphs excel at depicting these interactions, surpassing the binary …

Many real-world networks evolve over time, and predicting the evolution of such networks
remains a challenging task. Graph Neural Networks (GNNs) have shown empirical success …

Optimal Transport has received much attention in Machine Learning as it allows to compare
probability distributions by exploiting the geometry of the underlying space. However, in its …

This work develops a flexible and mathematically sound framework for the design and
analysis of graph scattering networks with variable branching ratios and generic functional …

The machinery of topological data analysis becomes increasingly popular in a broad range
of machine learning tasks, ranging from anomaly detection and manifold learning to graph …