85 points by datawhiz123 1 year ago flag hide 13 comments
john_doe 1 year ago next
Fascinating topic! I've been exploring GNNs recently and it's amazing how concepts from graph theory are influencing this field.
jane_doe 1 year ago next
Totally agree, John! The interplay of graph theory and neural networks could open up new opportunities for solving complex problem.
graph_wiz 1 year ago prev next
Undirected, directed, acyclic and edge-weighted graphs all have relevant counterparts in the design of Graph Neural Networks!
john_doe 1 year ago next
Interesting insight! Would love to see some real-world applications using this idea.
code_diva 1 year ago next
Here's a great paper on applying GNNs to chemo-informatics datasets: [url](https://arxiv.org/abs/1703.06303). It discusses using graphs for molecular analysis.
ml_enthusiast 1 year ago prev next
Some researchers are already using GNNs to improve performance in prediction and recommendation systems. This is really exciting!
random_user 1 year ago next
@ML_enthusiast can you please link some of these research papers you have mentioned? It would be great to read them.
researchscout 1 year ago next
Check out this survey paper for a comprehensive overview of recent advancements in GNNs: [url](<https://arxiv.org/abs/1812.08434>). It contains a lot of valuable information on both fundamentals and applications.
jan_meyer 1 year ago prev next
This is similar to attention mechanism! Like when you apply graph Attention Networks for traffic prediction.
ml_enthusiast 1 year ago next
@jan_meyer Precisely, there's a lot of common ground between graph theory principles and modern AI techniques. It's opening up unique possibilities.
graphical_whiz 1 year ago prev next
People often skip over isomorphism concepts when discussing GNNs, but they're going to be more important the deeper you go into graph neural networks.
programmingprophet 1 year ago next
Graph isomorphism is indeed interesting when thinking about concepts like permutation equivariance. Excited to see new techniques.
fastforward 1 year ago prev next
Utilizing isomorphism property opens way to learn from examples rather than explicit graph manipulation. Anyone thinking about scaling GNNs should experiment with this.