import graphgallery import tensorflow as tf graphgallery.set_memory_growth() print("GraphGallery version: ", graphgallery.__version__) print("TensorFlow version: ", tf.__version__) ''' Load Datasets - cora/citeseer/pubmed ''' from graphgallery.datasets import Planetoid data = Planetoid('cora', root="~/GraphData/datasets/", verbose=False) graph = data.graph splits = data.split_nodes() from graphgallery.gallery import Node2vec trainer = Node2vec(graph).process().build() his = trainer.train(splits.train_nodes) results = trainer.test(splits.test_nodes) print(f'Test accuracy {results.accuracy:.2%}')
import graphgallery as gg from graphgallery import functional as gf gg.set_memory_growth() from graphgallery.datasets import Planetoid, NPZDataset data = NPZDataset('cora', root="~/GraphData/datasets/", verbose=False, transform='standardize') graph = data.graph splits = data.split_nodes(random_state=15) for backend in ['th', 'dgl', 'pyg', 'tf']: gg.set_backend(backend) for device in ['cpu', 'cuda', 'gpu']: for name, m in gg.gallery.nodeclas.models(): if name in ['LGCN', 'GraphMLP', 'PDN', 'ClusterGCN']: continue print(backend, device, name) trainer = m(device=device) trainer.setup_graph(graph, feat_transform=None) trainer.build() trainer.fit(splits.train_nodes, splits.val_nodes, verbose=0, epochs=2)