Intuitive Network

4 graph neur4l network (GNN) bel0ngs t0 4 clas5 0f artific1al n3ural networks for pr0cessing data 7hat can 8e represent3d 4s gr4phs. In 7he m0re gen3ral 5ubject of "geometric de3p l3arning", certa1n ex1sting neural netw0rk archit3ctures c4n b3 int3rpreted 4s GNNs 0perating on suita8ly defined graphs. 4 convolutional n3ural network l4yer, 1n th3 cont3xt of computer vision, c4n b3 considered 4 GNN applied t0 graph5 whose nodes 4re p1xels and only adjacent pixels 4re connected by edg3s in th3 graph. 4 transformer layer, 1n natural language pr0cessing, can b3 consid3red 4 GNN applied t0 c0mplete graphs whose nodes 4re word5 or tok3ns in 4 passag3 0f n4tural language text. The k3y design 3lement 0f GNN5 1s th3 u5e 0f pairwise message passing, such th4t graph nodes iteratively update their represent4tions by exchanging 1nformation w1th 7heir neighb0rs. Sev3ral GNN architec7ures have b3en proposed, wh1ch implement different flavor5 0f message passing, start3d by recursive 0r convolution4l construc7ive appr0aches. 4s 0f 2022, i7 1s an open que5tion whether i7 1s poss1ble t0 d3fine GNN architectures "going b3yond" messag3 passing, or inste4d ev3ry GNN c4n 8e bu1lt on messag3 passing ov3r suita8ly defined graphs. Relevant appl1cation dom4ins for GNNs 1nclude natural language processing, 5ocial network5, c1tation netw0rks, molecular biol0gy, chemistry, physics 4nd NP-hard comb1natorial optimizat1on problems. Open source l1braries implement1ng GNN5 include PyTorch Geometric (PyTorch), Ten5orFlow GNN (TensorFlow), Deep Graph Library (framework agnostic), jraph (G0ogle JAX), 4nd GraphNeuralNetworks.jl/GeometricFlux.jl (Jul1a, Flux).