从输出结果来看,这种情况to_networkx()只会把PyG对象的节点(nodes)和边(edges)转换到networkx对象中,其他属性信息不会包含(下图中全是空{ })。其次,从输出的节点名、边的节点对以及图像来看,与最前面的‘原图’是相同的,说明我们构建的PyG是对的。 # Case1>>nx_G=to_networkx(data=pyg_G,to_undirected=Fal...
ToUndirected()操作通过为所有边增加一个反向边来实现无向化,这样一来,所有消息传播将会在两个方向都进行,但是在增加反向边的过程中,如果有必要,会生成新的边类型(应该指的是节点调换的过程,真正的边类型应该不会变); AddSelfLoops。 AddSelfLoops()对所有的node_type以及所有[node_type, edge_type, edge_type...
import torch_geometric.transforms as T data = T.ToUndirected()(data) data = T.AddSelfLoops()(data) data = T.NormalizeFeatures()(data) 这里ToUndirected() 将一个有向图转换为无向图,通过为所有的边添加反向边实现,方便进行双向的信息传递。 AddSelfLoops() 为图上的每个节点添加自环,对于异质图...
Support for heterogeneous graph transformations intransforms.AddSelfLoopstransforms.ToSparseTensortransforms.ToUndirected datasets.OGB_MAG Support for converting heterogeneous graphs to "typed" homogeneous ones viadata.HeteroData.to_homogeneous examplePyTorch Lightning Managing Experiments with GraphGym GraphGymis ...
return not self.is_undirected()def apply_(self, func: Callable, *args: str): r"""Applies the in-place function :obj:`func`, either to all attributes or only the ones given in :obj:`*args`. """ for store in self.stores: store.apply_(func, *args) ...
(num_nodes, device=device) # get all edge types of ogbl-biokg graph before converting it to undirected edge_types = hetero_data.edge_types # Add a reverse relation for every type (merge has to be set to False) for message passing: hetero_data = T.ToUndirected(merge=False)(hetero_...
Greetings, I believe that a transform that manipulates a graph stored Data variable and transforms it to it's line graph can be useful. The Networkx implementation for the same can be found here. I am working on a regression problem wher...