41 citations · 95 across the 19 of their papers we have counts for
5 papers · 1 filter
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
Ruikang Ouyang, Andrew Elliott, Stratis Limnios +2
For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representati…
Robust Angular Synchronization via Directed Graph Neural Networks
Yixuan He, Gesine Reinert, David Wipf +1
The angular synchronization problem aims to accurately estimate (up to a constant additive phase) a set of unknown angles from noisy measurements of…
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu +3
Over recent years, denoising diffusion generative models have come to be considered as state-of-the-art methods for synthetic data generation, especially in the case of generating…
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
Yixuan He, Xitong Zhang, Junjie Huang +3
Networks are ubiquitous in many real-world applications (e.g., social networks encoding trust/distrust relationships, correlation networks arising from time series data). While man…
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Yixuan He, Quan Gan, David Wipf +3
Recovering global rankings from pairwise comparisons has wide applications from time synchronization to sports team ranking. Pairwise comparisons corresponding to matches in a comp…