I think it is a very nice proposal. Today the Machine Learning / Deep Learning on top of the linked data is very hot topic and any storage layer like GAR should think about support of it.
On Sat, 2026-02-21 at 16:11 +0300, Iskander Fakhrutdinov wrote: > > Hi everyone, > > I'd like to propose adding a new API to GraphAr that enables > resource-efficient data retrieval for GNN (graph neural network) > training workloads. > > The motivation comes from recent work > <https://arxiv.org/pdf/2411.11375> on scaling GNN training via graph > databases. Their approach demonstrates memory savings but shows some > bottlenecks (e.g., result conversion overhead). This proposal takes > the same core idea and implements it as optimized operations directly > at the GraphAr layer. > > I've discussed this idea with a few PPMC members, including Sem > Sinchenko, who has agreed to shepherd the proposal. > > The full proposal document is here > <https://docs.google.com/document/d/1oLShCWa9s__OItmwORglzm4oQoig4lzq > JQZ1ZcpiEZY/edit?usp=sharing>. I would appreciate any feedback on the > scope, approach, and design direction. > > > Thanks, > Iskander Fakhrutdinov --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
