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[N] Deep Graph Library new release (v0.3.1)

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

New global pooling module

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.

submitted by /u/jermainewang
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.