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

Current technology is producing high throughput biomedical data at an ever-growing rate. A
common approach to interpreting such data is through network-based analyses. Since …

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 …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Machine learning on graphs is an important and ubiquitous task with applications ranging
from drug design to friendship recommendation in social networks. The primary challenge in …

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 …

Hyperbolic embeddings offer excellent quality with few dimensions when embedding
hierarchical data structures. We give a combinatorial construction that embeds trees into …

We introduce hyperbolic attention networks to endow neural networks with enough capacity
to match the complexity of data with hierarchical and power-law structure. A few recent …

The quality of the representations achieved by embeddings is determined by how well the
geometry of the embedding space matches the structure of the data. Euclidean space has …