Though only a minor release, this new release includes a bunch of very useful Graph Neural Network modules and model examples that can be directly used in your project. Here is a list of new modules:

New NN Modules

• GATConv from “Graph Attention Network”
• RelGraphConv from “Modeling Relational Data with Graph Convolutional Networks”
• TAGConv from “Topology Adaptive Graph Convolutional Networks”
• EdgeConv from “Dynamic Graph CNN for Learning on Point Clouds”
• SAGEConv from “Inductive Representation Learning on Large Graphs”
• GatedGraphConv from “Gated Graph Sequence Neural Networks”
• GMMConv from “Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs”
• GINConv from “How Powerful are Graph Neural Networks?”
• ChebConv from “Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering”
• SGConv from “Simplifying Graph Convolutional Networks”
• NNConv from “Neural Message Passing for Quantum Chemistry”
• APPNPConv from “Predict then Propagate: Graph Neural Networks meet Personalized PageRank”
• AGNNConv from “Attention-based Graph Neural Network for Semi-Supervised Learning”
• DenseGraphConv (Dense implementation of GraphConv)
• DenseSAGEConv (Dense implementation of SAGEConv)
• DenseChebConv (Dense implementation of ChebConv)

New global pooling module

• Sum/Avg/MaxPooling
• SortPooling
• GlobalAttentionPooling from GGNN model
• Set2Set from “Order Matters: Sequence to sequence for sets”
• SetTransformerEncoder and SetTransformerDecoder from “Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks”

New graph transformation routines

• dgl.transform.khop_adj
• dgl.transform.khop_graph
• dgl.transform.laplacian_lambda_max
• dgl.transform.knn_graph
• dgl.transform.segmented_knn_graph

This DGL release also includes a model zoo for chemistry applications such as using GNNs to predict molecular property or generate new molecule structures that is valuable for drug discovery. Pre-trained models are also available for download in simply two lines of codes:

“`python from dgl.data import Tox21 from dgl import model_zoo

dataset = Tox21() model = model_zoo.chem.load_pretrained(‘GCN_Tox21’) # Pretrained model loaded model.eval()

smiles, g, label, mask = dataset[0] feats = g.ndata.pop(‘h’) label_pred = model(g, feats) print(smiles) # CCOc1ccc2nc(S(N)(=O)=O)sc2c1 print(label_pred[:, mask != 0]) # Mask non-existing labels

tensor([[-0.7956, 0.4054, 0.4288, -0.5565, -0.0911,

0.9981, -0.1663, 0.2311, -0.2376, 0.9196]])

“`

Check it out if you are using GNNs, working with molecules or just interested in this whole new field.

See full release note here: https://www.dgl.ai/release/2019/08/28/release.html.