Comments (4)
You only need to change the loss function. Rather than one softmax followed by CE/max margin, you have several of these heads, and sum over the loss.
I think it makes sense to use the same graphsage embedding to predict multiple labels, unless you have clear prior knowledge that each label should be based on very different sources of information and you want to learn each separately.
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Thanks, that makes sense.
Did you try with any multi-label dataset? Refer to mine, and my friends result that SAGE doesn't work well as expected for multi-label classification task.
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What's the baseline that you use to compare with GraphSAGE? Multi-label task could be harder if an instance is correctly classified only when all the labels are correctly classified.
Did you compare with baseline that's also a mult-label classification algorithm?
I do not yet see why multi-label tasks are less suitable for GraphSAGE.
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Sorry for my late.
I use doc2vec + logistic regression as the baseline. It means I apply doc2vec on node attributes (words) to work as node embedding.
For SAGE, I use the embedding output from doc2vec as node attribute. But after SAGE, node embedding works less good compared to the result of doc2vec, with the same classifier.
This is what confuses me. I try to tune the graph structure (number of layers, batch size, embedding size etc.) but doesn't work. There might be two reasons: one is my network is not suitable for SAGE, second is SAGE works not good for multi-label.
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Related Issues (20)
- Fatal Python error: Segmentation fault
- Is there any example notebooks of using this code?
- What's the difference between GraphSAGE-GCN and GraphSAGE-mean ?
- Norm of node representation z
- how sage train the node that only have 1 top neighbors? HOT 3
- 异构图生成二部图问题Graph is multigraph, cannot be converted to networkx graph. HOT 1
- What if the number of neighbors is less than the pre-defined number? HOT 1
- GraphSAGE for weighted graphs
- PPI ID-protein name correspondence?
- Unseen Node classification HOT 3
- Question about PPI: how to process C1, C3 and C7 into 50-dim features?
- WHY 2 layer loop in aggregate function?
- I'm new to graphsage. Can't run. Getting error.Please explain HOT 1
- Dense layers use relu with xavier initialization
- the code problem in utils HOT 1
- networkx issue with reddit dataset HOT 2
- what is the class in "<train_prefix>-class_map.json" means. HOT 1
- why i still can't run the program because an error No such file or directory: './example_data/ppi-G.json' HOT 1
- how to change the dataset HOT 1
- BLAS and GEMM occur error
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