v0.6.21

GraphNeuralNetworks v0.6.21

Diff since v0.6.20

Merged pull requests:

• Add DCGRU temporal layer (#448) (@aurorarossi)
• move mldatasets2gnngraph (#458) (@CarloLucibello)
• create GNNLux.jl package (#460) (@CarloLucibello)
• [GNNLux] GCNConv, ChebConv, GNNChain (#462) (@CarloLucibello)
• [GNNLux] more layers (#463) (@CarloLucibello)
• use GNNlib in GNN.jl (#464) (@CarloLucibello)
• [GNNlib] fix cuda ext (#465) (@CarloLucibello)
• tests for GNNlib (#466) (@CarloLucibello)
• fix docs (#467) (@CarloLucibello)
• [GNNLux] fix tests (#468) (@CarloLucibello)
• [GNNLux] more layers (#469) (@CarloLucibello)
• [GNNLux] TGCN temporal layer (#470) (@aurorarossi)
• [GNNLux] more layers pt. 3 (#471) (@CarloLucibello)
• [GNNGraphs] implement remove_edges(g, p) (#474) (@rbSparky)
• [GNNLux] Added SGConv (#475) (@rbSparky)
• fix dense test (#479) (@CarloLucibello)
• [GNNLux] Adding MegNetConv Layer (#480) (@rbSparky)
• rng instead of seed for rand_graph (#482) (@CarloLucibello)
• @functor ->@layer (#484) (@CarloLucibello)
• [GNNLux] Add A3TGCN temporal layer (#485) (@aurorarossi)
• [GNNLux] Add GConvLSTM, GConvGRU and DCGRU temporal layers (#487) (@aurorarossi)
• fix NNConv docs (#488) (@CarloLucibello)
• Add EvolveGCNO temporal layer (#489) (@aurorarossi)
• [GNNLux] updates for Lux v1.0 (#490) (@CarloLucibello)
• [GNNLux] Adding NNConv Layer (#491) (@rbSparky)
• [GNNLux] add GMMConv, ResGatedGraphConv (#494) (@CarloLucibello)

Closed issues:

• Question about temporal graph neural networks (#112)
• add show methods for WithGraph and GNNChain (#178)
• Dropout inside GATConv layer (#258)
• Graph classification: multiple graphs associated with a common label (#282)
• convenience feature setter (#284)
• @functor default for GNNLayers (#288)
• documentation proposal (#357)
• support Lux (#372)
• turn this into a monorepo (#433)
• use Flux.@layer instead of Flux.@functor (#452)
• random graph generators should take an rng instead of a seed (#459)
• Cannot create GNNGraph with unconnected nodes (#472)
• Implementation of recommender system based on GNN (#473)
• GNNs.jl's CI is failing for GRAPH_T = :dense (#476)
• GCNConv layer fails when the GNNGraph comes from an adjacency matrix (#486)