Hyperbolic graph neural networks: A review of methods and applications
Graph neural networks generalize conventional neural networks to graph-structured data
and have received widespread attention due to their impressive representation ability. In …
and have received widespread attention due to their impressive representation ability. In …
COSTA: covariance-preserving feature augmentation for graph contrastive learning
Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA
on various downstream tasks. The graph augmentation step is a vital but scarcely studied …
on various downstream tasks. The graph augmentation step is a vital but scarcely studied …
Mitigating the popularity bias of graph collaborative filtering: A dimensional collapse perspective
Abstract Graph-based Collaborative Filtering (GCF) is widely used in personalized
recommendation systems. However, GCF suffers from a fundamental problem where …
recommendation systems. However, GCF suffers from a fundamental problem where …
HRCF: Enhancing collaborative filtering via hyperbolic geometric regularization
In large-scale recommender systems, the user-item networks are generally scale-free or
expand exponentially. For the representation of the user and item, the latent features (aka …
expand exponentially. For the representation of the user and item, the latent features (aka …
Hyperbolic graph neural networks: A tutorial on methods and applications
Graph Neural Networks (GNNs) generalize conventional neural networks to graph-
structured data and have received considerable attention owing to their impressive …
structured data and have received considerable attention owing to their impressive …
Hicf: Hyperbolic informative collaborative filtering
Considering the prevalence of the power-law distribution in user-item networks, hyperbolic
space has attracted considerable attention and achieved impressive performance in the …
space has attracted considerable attention and achieved impressive performance in the …
Hyperbolic representation learning: Revisiting and advancing
The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable
attention in the realm of representation learning. Current endeavors in hyperbolic …
attention in the realm of representation learning. Current endeavors in hyperbolic …
Contrastive cross-scale graph knowledge synergy
Graph representation learning via Contrastive Learning (GCL) has drawn considerable
attention recently. Efforts are mainly focused on gathering more global information via …
attention recently. Efforts are mainly focused on gathering more global information via …
Dynamically expandable graph convolution for streaming recommendation
Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …
information overload and satisfy users' diverse needs. However, conventional …
Bipartite graph convolutional hashing for effective and efficient top-n search in hamming space
Searching on bipartite graphs is basal and versatile to many real-world Web applications,
eg, online recommendation, database retrieval, and query-document searching. Given a …
eg, online recommendation, database retrieval, and query-document searching. Given a …