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palash1992 / GEM

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Error making link prediction #47

Closed zuozijiZHOULI closed 5 years ago

zuozijiZHOULI commented 5 years ago

Hi,when I modified test.kerate.py to make link prediction, the following error occurred: Traceback (most recent call last): File "test_karate.py", line 54, in MAP, prec_curv = lp.evaluateStaticLinkPrediction(G, embedding,0.8) File "/home/public/anaconda3/lib/python3.6/site-packages/gem-1.0.0-py3.6.egg/gem/evaluation/evaluate_link_prediction.py", line 24, in evaluateStaticLinkPrediction is_undirected=is_undirected File "/home/public/anaconda3/lib/python3.6/site-packages/gem-1.0.0-py3.6.egg/gem/utils/evaluation_util.py", line 50, in splitDiGraphToTrainTest train_digraph.remove_edge(ed, st) File "/home/public/anaconda3/lib/python3.6/site-packages/networkx/classes/digraph.py", line 692, in remove_edge raise NetworkXError("The edge %s-%s not in graph."%(u,v)) networkx.exception.NetworkXError: The edge 31-0 not in graph. I modified it as follows: MAP, prec_curv = lp.evaluateStaticLinkPrediction(G, embedding,0.8) I would appreciate it if you could help me!

palash1992 commented 5 years ago

As the graph is directed, please set is_undirected=False

zuozijiZHOULI commented 5 years ago

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?

palash1992 commented 5 years ago

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.

zuozijiZHOULI commented 5 years ago

Is this input the vector of all the nodes, or the vector of the nodes in the training data?

palash1992 commented 5 years ago

The nodes in the training data and test data are the same as the splitting is done on edges, not nodes.