Continual graph learning routinely finds its role in a variety of real-world applications where
the graph data with different tasks come sequentially. Despite the success of prior works, it …

Recent methods in geometric deep learning have introduced various neural networks to
operate over data that lie on Riemannian manifolds. Such networks are often necessary to …

Graphs are typical non-Euclidean data of complex structures. Recently, Riemannian graph
representation learning emerges as an exciting alternative to the traditional Euclidean ones …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …

Graph representation learning received increasing attentions in recent years. Most of the
existing methods ignore the complexity of the graph structures and restrict graphs in a single …

Graph Neural Networks usually rely on the assumption that the graph topology is available
to the network as well as optimal for the downstream task. Latent graph inference allows …

In this paper, we comparatively analyze the Bures-Wasserstein (BW) geometry with the
popular Affine-Invariant (AI) geometry for Riemannian optimization on the symmetric positive …

We study representations of data from an arbitrary metric space χ in the space of univariate
Gaussian mixtures equipped with a transport metric (Delon and Desolneux 2020). We prove …

Discriminative subspace learning is an important problem in machine learning, which aims
to find the maximum separable decision subspace. Traditional Euclidean-based methods …

Graph embeddings, wherein the nodes of the graph are represented by points in a
continuous space, are used in a broad range of Graph ML applications. The quality of such …