[HN Gopher] Beyond message passing: A physics-inspired paradigm ... ___________________________________________________________________ Beyond message passing: A physics-inspired paradigm for graph neural networks Author : andreyk Score : 57 points Date : 2022-05-09 18:04 UTC (4 hours ago) (HTM) web link (thegradient.pub) (TXT) w3m dump (thegradient.pub) | phonebucket wrote: | Can anyone recommend any arXiv/paper links on the subject for | someone with reasonable prerequisties (e.g. neural ODEs, physics | informed neural networks and message passing)? The number of | references in the article is a bit of an overload! Looks like | fascinating field. | ssivark wrote: | From a quick glance, the blog post seems to be based on the | following paper involving the same author: | https://arxiv.org/abs/2106.10934 | andreyk wrote: | A survey paper is usually a good way to go, such as A | comprehensive survey on graph neural networks | (https://arxiv.org/abs/1901.00596) or Graph Neural Networks: A | Review of Methods and Applications | (https://arxiv.org/abs/1812.08434) | albertzeyer wrote: | Can someone explain the downvotes here? If you think these | are bad papers, maybe recommend some better ones? Or what is | wrong with this post? | hasmanean wrote: | So when will we have message-passing processor architectures | again? | sandGorgon wrote: | anyone running graph neural networks in production ? what | framework do you use ? | dil8 wrote: | Check out dgl (https://github.com/dmlc/dgl). A lot of papers | and algorithms are implemented in the examples section. | melony wrote: | Are implementations of belief propagation considered message | passing GNNs? | andreyk wrote: | Pretty sure that's the case, yeah ___________________________________________________________________ (page generated 2022-05-09 23:00 UTC)