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

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 …

Named Data Networking (NDN) is one of five projects funded by the US National Science
Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier …

We develop a geometric framework to study the structure and function of complex networks.
We assume that hyperbolic geometry underlies these networks, and we show that with this …

Hyperbolic neural networks have shown great potential for modeling complex data.
However, existing hyperbolic networks are not completely hyperbolic, as they encode …

The Internet infrastructure is severely stressed. Rapidly growing overheads associated with
the primary function of the Internet—routing information packets between any two computers …

While numerous approaches have been developed to embed graphs into either Euclidean
or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the …

The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable
attention in the realm of representation learning. Current endeavors in hyperbolic …

We show that complex (scale-free) network topologies naturally emerge from hyperbolic
metric spaces. Hyperbolic geometry facilitates maximally efficient greedy forwarding in these …