(1) THE increasing importance of hyperbolic functions in several branches of science and technology has led the Smithsonian Institution to furnish the computer with a more complete set of tables of ...

Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existing GNNs embed graph data into Euclidean space and thus are less effective to capture the ubiquitous ...

Graph Convolutional Neural Networks (GCNs) embed graph data into either Euclidean or non-Euclidean spaces. For power-law distributed graphs, Euclidean embeddings distort input features because they ...

THROUGHOUT the books treating of hyperbolic functions, although elaborate series for their determination are given, the possibility of calculating them directly from their definitions, by means of ...

This repository is a graph representation learning library, containing a modified implementation of Hyperbolic Graph Convolutional Networks (HGCN) [1] as well as original (HGCN) method. Old_HGCN: This ...

Abstract: Social recommendation provides an auxiliary social network structure to enhance recommendation performances. By formulating user-user social network and user-item interaction graph, modern ...

Mathematics Magazine presents articles and notes on undergraduate mathematical topics in a lively expository style that appeals to students and faculty throughout the undergraduate years. The journal ...

With the rapid growth of mobile network and Artificially Intelligence Generated Content (AIGC), the spread of fake news has intensified, making the identification of credible information increasingly ...