Comments (2)
Thanks for your question. Here the implementation is inspired by word2vec, in which the inner product of a pair of words within a window is optimized. If we directly optimize the relevance probability, the inner product terms in the denominator will be likely to decrease, which is not what we want since they are also ground truth edges in the graph.
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看完代码,我也发现这点。所以,我也有相同的疑问。能不能麻烦作者帮我们答疑一下,谢谢了。
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Related Issues (20)
- pretrained embeddings HOT 1
- could you share the code of Node classification?
- A serious BUG? HOT 1
- 为什么link prediction evalue的时候,准确率一直下降呢 HOT 10
- How can you get the test_neg.txt file? HOT 1
- Error of reading the pre_trained embedding?
- 关于emb初始化
- No module named src HOT 5
- Running too slow, can use gpu to speed up
- 请问节点分类的数据集是使用哪一个呢?可以共享吗? HOT 2
- FileNotFoundError: [Errno 2] No such file or directory: '../../results/link_prediction/CA-GrQc_gen_.emb' HOT 3
- rien
- I have two questions
- Evaluation of the link prediction for D and G
- I reimplement this paper and add other two experiments HOT 1
- FileNotFoundError: [Errno 2] No such file or directory: '../../cache/CA-GrQc.pkl' how to solve this problem? HOT 2
- 公式7->公式8的推导问题
- Generator and Discriminator embeddings
- Number of Nodes in the pre-training embedding file
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