Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

Despite the success of traditional network analysis, standard networks provide a limited
representation of complex systems, which often include different types of relationships (or …

Graph convolutional neural networks (GCNs) embed nodes in a graph into Euclidean space,
which has been shown to incur a large distortion when embedding real-world graphs with …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

Learning from graph-structured data is an important task in machine learning and artificial
intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

Abstract Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, state-of-the-art embedding methods …

Hyperbolic spaces have recently gained momentum in the context of machine learning due
to their high capacity and tree-likeliness properties. However, the representational power of …

We argue word embedding models are a useful tool for the study of culture using a historical
analysis of shared understandings of social class as an empirical case. Word embeddings …

We are concerned with the discovery of hierarchical relationships from large-scale
unstructured similarity scores. For this purpose, we study different models of hyperbolic …