39 citations · 81 across the 19 of their papers we have counts for
3 papers · 1 filter
Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
Michal Balcerak, Suprosanna Shit, Chinmay Prabhakar +4
Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, p…
Differentially Private Graph Classification with GNNs
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar +3
Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein inte…
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit +9
Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is…