Search Results for author:
Found 6 papers, 4 papers with code
GraKeL: A Graph Kernel Library in Python
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods.
Rep the Set: Neural Networks for Learning Set Representations
In several domains, data objects can be decomposed into sets of simpler objects.
Ranked #1 on
Document Classification
on Twitter
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
For analysing real-world networks, graph representation learning is a popular tool.
Hcore-Init: Neural Network Initialization based on Graph Degeneracy
As a multipartite graph is a combination of bipartite graphs, that are in turn the incidence graphs of hypergraphs, we design k-hypercore decomposition, the hypergraph analogue of k-core degeneracy.
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
SaGess then constructs a synthetic graph using the subgraphs that have been generated by DiGress.
