Comments (2)
Hello
The problem I urgently need to solve is exactly similar to your goal. I generated the embedding in another way. I need to solve the task of link prediction. Could you please solve it.
Any answer will be of great help to me.
Thank you
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I have solved issues regarding link prediction by changing the following files and the following lines:
- File /gem/utils/evaluation.util.py
Line 40:for (st, ed, w) in di_graph.edges_iter(data='weight', default=1):
I have changed to for (st, ed, w) in list(di_graph.edges(data='weight', default=1)):
Reason: As far as I have understood, edges_iter was removed after NetworkX 2.0.
- File /gem/evaluation/evaluate_link_prediction.py
Line 67:filtered_edge_list = [e for e in predicted_edge_list if not train_digraph.has_edge(node_l(e[0]), node_l(e[1]))]
I have changed to filtered_edge_list = [e for e in predicted_edge_list if not train_digraph.has_edge(node_l[e[0]], node_l[e[1]])]
Reason: If node_l is None (line 65), then node_l is defined as a list (line 66). Therefore, as lists being not callable, one must use node_l[...], and not node_l(...). I have not tested link prediction for labeled graphs yet.
Best regards!
from gem.
Related Issues (20)
- Error in SDNE
- Tensorflow module is missing
- sdne: Nodes corresponding to embedded vectors HOT 1
- Problem with dependencies HOT 3
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- The use of "merge" module of keras.layers in sdne.py HOT 2
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