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It seems GNNs operate on a fixed topology. What if I want to approximate some transformation of the topology of the graph? For example learning how to layout a
by helltone 2y ago
It seems GNNs operate on a fixed topology. What if I want to approximate some transformation of the topology of the graph? For example learning how to layout a graph, or converting program abstract syntax trees to data flow graphs.
- igorkraw 2y agoThe whole point of GNNs is that they generalize to arbitrary topologies by explicitly conditioning the idea of "neighbours" on the graph specifying the topology. Graph layout has been tried here https://github.com/limbo018/DREAMPlace https://github.com/limbo018/DREAMPlace to great fanfare although there is recent drama about it https://www.semanticscholar.org/paper/The-False-Dawn%3A-Reevaluating-Google's-Reinforcement-Markov/c43045140f4b30def64f04d1bc24a14030c0798b https://www.semanticscholar.org/paper/The-False-Dawn%3A-Reev... . Graph transformations are a thing as well https://arxiv.org/abs/2012.01470 https://arxiv.org/abs/2012.01470 but it's a tricky problem because you implicitly need to solve the graph matching problem
- Xmd5a 2y agoGraph layout is extremely interesting for infographics, since a handmade graph will almost always beat what tools such as graphviz can come up with (and I'm not even mentioning algorithms that go beyond Sugiyama's for which there is only a paper). Any progress on this front ?
- FjordWarden 2y agoMaybe homology can help, it is a sort of calculus for discrete structures where you count how many N dimensional hole there are over time. Dunno about NN but that is what they can do with fMRI.