Text-to-GRAPH w/ LGGM: Generative Graph Models 

For me graphs and specially AMR graphs (Abstract Meaning Representation) are the key to enable reasoning capabilities and move out of the stochastic parrot

It's important to note that while diffusion is used, and you've covered this in the past, they use ~~discrete~~ diffusion, not the continuous diffusion that is used in imaging, video, audio, etc. In discrete diffusion, the data are multinomials; practically, this means that during the noising process of graphs, they can drop or add random edges or nodes, rather than adjusting their strengths (as would be done in continuous diffusion). Also, while "Text2graph" is exciting, this is not "convert an arbitrary text stream into a knowledge graph," but rather "create a graph with the kind of properties that I describe in text," that is, "create a graph with large numbers of loops", trees, simplexes of particular order etc., at least as seen in what they show us. Perhaps, however, converting text to knowledge graphs is part of the plan (Adobe, Intel, wink, wink, nudge, nudge).

Inria researchers published a very elegant similar paper called CARTE, they don’t use this method it’s a different idea. Maybe an idea for a future cover? Thank you so much for your content

where do i need graph 🤔 stocks market. Stocks analysts literally predicting where C be after A+B and such podcasts are not free.