Comments (6)
https://pypi.python.org/pypi/networkx/1.9.1
here is the .whl file you can download
before you install the old version of networkx, remember to uninstall your latest networkx
and then use pip3 install networkx-1.9.1-py2.py3-none-any.whl
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you can see the file in data folder,but i had a problem when i run the code
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AttributeError: 'Graph' object has no attribute '_node'
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@mellinging you can change you version of package networkx, I have the same problem and I try on another PC, this bug missing.
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Hello, I wonder whether the output is a graph too, because when I try to draw it using networkx1.9, there are some error in plt
@Uylee you should change version of network if there occurs some errors. you can reference my another answer.
@mellinging you can change you version of package networkx, I have the same problem and I try on another PC, this bug missing.
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Hello, everyone!
Here is the link of the official documents for Networkx about how to deal with the version problem:
https://networkx.github.io/documentation/latest/release/migration_guide_from_1.x_to_2.0.html
and here is the code below:
in v1.x
pickle.dump([G.nodes(data=True), G.edges(data=True)], file)
then in v2.x
nodes, edges = pickle.load(file)
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)
the graphs in 1.x and 2.x are different. So, we need to change the version to 1.x or change the graph given, all will work!!!
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Related Issues (19)
- loss function different from the paper HOT 2
- How can i get embedding between 0-1? HOT 4
- Question about negative sampling in utils.py HOT 5
- low efficiency of `tf.matmul`
- second order similarity issue HOT 2
- hi,i am confused about the loss in your code. HOT 1
- run on multiple cores? HOT 1
- About the embeddings of second-order similarity HOT 2
- data replace HOT 6
- the program can't run HOT 1
- Hello
- Run on CPU HOT 1
- Loss only decrease from 2.2 to 1.8 HOT 2
- 数据集 HOT 1
- can i only maintain the second-order proximity?
- sorry, this is not an issue. just my mistake.
- Some errors occur HOT 1
- if not self.g.has_edge(self.node_index_reversed[negative_node], self.node_index_reversed[edge[0]]), can you get negative samples for second-order ?
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