Comments (5)
As the graph is directed, please set is_undirected=False
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hi,Palash Goyal
When I modify isdirected=False, there are no errors, I get the results as follows:
An algorithmic framework for representational learning on graphs. [Apr 9 2017]
Input graph path (-i:)=tempGraph.graph
Output graph path (-o:)=tempGraph.emb
Number of dimensions. Default is 128 (-d:)=2
Length of walk per source. Default is 80 (-l:)=80
Number of walks per source. Default is 10 (-r:)=10
Context size for optimization. Default is 10 (-k:)=10
Number of epochs in SGD. Default is 1 (-e:)=1
Return hyperparameter. Default is 1 (-p:)=1
Inout hyperparameter. Default is 1 (-q:)=1
Verbose output. (-v)=YES
Graph is directed. (-dr)=YES
Graph is weighted. (-w)=YES
Read 126 lines from tempGraph.graph
Preprocessing progress: 0.00%
Walking Progress: 0.00%
Learning Progress: 75.76%
MAP: 0.2912400920513133 preccision curve: [0.0, 0.0, 0.0, 0.0, 0.0]
But I don't think it's right.
At present, I have my own way of network representation learning, and it has been able to represent my own network data as vectors. Now I want to make link prediction to evaluate its effect.However, I don't understand link prediction very well.
When evaluate the Link Prediction,What is the data format it input? Is it source target?Does it need to be labeled?
from gem.
The output above is correct.
The code takes the embedding method class object, graph and the obtained vector as inputs. It doesn't need to be labeled.
from gem.
Is this input the vector of all the nodes, or the vector of the nodes in the training data?
from gem.
The nodes in the training data and test data are the same as the splitting is done on edges, not nodes.
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
- LaplacianEigenmap for a large number of connected components HOT 3
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- The use of "merge" module of keras.layers in sdne.py HOT 2
- EXECUTION HOT 3
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- gf not found HOT 2
- Use of Link prediction code HOT 2
- The number of positive classes for each node is leaked to TopKRanker HOT 1
- How to determine dimension variable in methods HOT 1
- Create embeddings directly from an adjacency matrix (e.g. numpy.array or scipy.sparse)? HOT 2
- [Errno 2] No such file or directory: 'gem/intermediate/karate_gf.graph' HOT 7
- SDNE implementation error HOT 3
- Error when running link prediction
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