图神经网络GNN：给图多个 node features和edge features

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''' 摘自https://docs.dgl.ai/en/0.6.x/guide_cn/graph-feature.html '''   import dgl import torch as th   # ========================= 无权图 ======================================   g = dgl.graph(([0, 0, 1, 5], [1, 2, 2, 0])) # 6个节点，4条边   # each graph can have many 'node features' g.ndata['x'] = th.ones(g.num_nodes(), 3)   # 节点特征x, 特征长度为3 g.ndata['y'] = th.randn(g.num_nodes(), 5)  # 节点特征y，特征长度为5   # similarly, each graph can have many 'edge features' g.edata['x'] = th.ones(g.num_edges(), dtype=th.int32)  # 标量整型边特征x g.edata['z'] = th.ones(g.num_edges(), dtype=th.float32)  # 浮点型型边特征z   print('g:\n', g)   print(g.ndata['x'][1])     # 获取节点特征x的节点1特征 print(g.ndata['y'][1])     # 获取节点特征y的节点1特征   print(g.edata['x'][th.tensor([0, 3])])  # 获取边特征x下的0和3节点特征 print()     # ========================= 有权图 ======================================   # edges 0->1, 0->2, 0->3, 1->3 edges = th.tensor([0, 0, 0, 1]), th.tensor([1, 2, 3, 3]) weights = th.tensor([0.1, 0.6, 0.9, 0.7])  # weight of each edge g = dgl.graph(edges) g.edata['w'] = weights  # give it a name 'w' print('weighted graph:\n', g)