Open ghost opened 6 years ago
Increasing the -walk_times (maybe > 10 or 20) and -sample_times usually helps.
BTW, if you test it on a public dataset, please tell me which dataset you used. I am willing and glad to have a check.
I just test on the amazon dataset refer to the original paper Scalable Graph Embedding for Asymmetric Proximity, but I can not get the result in the Node Recommendation experiment, and the result far less than the result in the paper
my running parameters is:
../cli/app -train net.txt -save rep_app.txt -undirected 1 -dimensions 128 -walk_times 20 -walk_steps 40 -window_size 5 -negative_samples 20 -alpha 0.025 -threads $Threads
Could you give me a comment if you have time to check and get any result? thank you very much
I believe that the implementation of APP is a bit incorrect. You should also save the context embeddings as a node has two representations in the source and target role.
Thanks for the feedbacks, I'll have a check.
in addition , do you know the best parameters for Cora dataset? I am unable to reproduce the paper's results for Cora even after using the context embeddings.
do you have the link of the dataset? I can test it later
here you go : http://konect.uni-koblenz.de/networks/subelj_cora
does it like the following:
../cli/app -train net.txt -save rep_dw.txt -undirected 1 -dimensions 64 -walk_times 1 -walk_steps 40 -window_size 5 -negative_samples 5 -alpha 0.025 -threads $Threads
because I can not run any good result under this method