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Method srctypes

python/dgl/heterograph.py:1090–1138  ·  view source on GitHub ↗

Return all the source node type names in this graph. If the graph can further divide its node types into two subsets A and B where all the edeges are from nodes of types in A to nodes of types in B, we call this graph a *uni-bipartite* graph and the nodes in A being the *sou

(self)

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1088
1089 @property
1090 def srctypes(self):
1091 """Return all the source node type names in this graph.
1092
1093 If the graph can further divide its node types into two subsets A and B where
1094 all the edeges are from nodes of types in A to nodes of types in B, we call
1095 this graph a *uni-bipartite* graph and the nodes in A being the *source*
1096 nodes and the ones in B being the *destination* nodes. If the graph is not
1097 uni-bipartite, the source and destination nodes are just the entire set of
1098 nodes in the graph.
1099
1100 Returns
1101 -------
1102 list[str]
1103 All the source node type names in a list.
1104
1105 See Also
1106 --------
1107 dsttypes
1108 is_unibipartite
1109
1110 Examples
1111 --------
1112 The following example uses PyTorch backend.
1113
1114 >>> import dgl
1115 >>> import torch
1116
1117 Query for a uni-bipartite graph.
1118
1119 >>> g = dgl.heterograph({
1120 ... ('user', 'plays', 'game'): (torch.tensor([0]), torch.tensor([1])),
1121 ... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
1122 ... })
1123 >>> g.srctypes
1124 ['developer', 'user']
1125
1126 Query for a graph that is not uni-bipartite.
1127
1128 >>> g = dgl.heterograph({
1129 ... ('user', 'follows', 'user'): (torch.tensor([0]), torch.tensor([1])),
1130 ... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
1131 ... })
1132 >>> g.srctypes
1133 ['developer', 'game', 'user']
1134 """
1135 if self.is_unibipartite:
1136 return sorted(list(self._srctypes_invmap.keys()))
1137 else:
1138 return self.ntypes
1139
1140 @property
1141 def dsttypes(self):

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Calls 1

keysMethod · 0.45

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