Comments (9)
1、emb[:,0]是否是只取每个emb的第0维度的值。
2、完全相同是指不同节点的值完全相同吗?可以给出一些运行结果吗?
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比如第145个epoch (我用的best_config). [ 8185 10778 12513 17729] 节点的emb完全一样。比较困惑
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1、emb[:,0]是否是只取每个emb的第0维度的值。
只用这个判断,然后取出对应的emb行再判断
2、完全相同是指不同节点的值完全相同吗?可以给出一些运行结果吗?
比如第145个epoch (我用的best_config). [ 8185 10778 12513 17729] 节点的emb完全一样。比较困惑
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复现方法:
python main.py -m HetGNN -d academic4HetGNN -t node_classification -g 0 --use_best_config --load_from_pretrained
在每个epoch保存emb
然后在181个epoch 看到如下节点id的emb完全一致
[ 0 1077 3246 7980 12413 16949 24381 28624]
可用上面脚本片段测试
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请问有啥进展吗?还是我哪里理解的不对
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1、我复现了实验,确认存在这个问题。
2、我查看了原文与代码。
最后的embedding是由content embedding与neighbor embedding组成;
查看了数据集本身,content embedding存在比较多的节点是由重复特征的,即content embedding重复;
要使得最后的结果节点完全一致,节点的neighbor embedding也要相同,即采样出的邻居相同,依据其采样算法,以及数据集特性,在某些节点邻居比较少时,其采样到的邻居会比较固定,造成其邻居一样的结果。
综上所述,我认为可能是该数据集可能会造成该问题。
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感谢回复,还有一点困惑的是在节点分类任务中,academic4HetGNN数据集训练到epoch=1 (use best config)即达到acc 95%以上, 后面在慢慢涨到97% 是否正常。
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该数据集中的特征包涵deep walk获得的embedding,该embedding是依据图结构获得的,换言之不经过模型,输入特征至下游任务也能获得较好的性能。
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thx
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