Comments (2)
A one hot init on the complete vocabulary would be something like the identity matrix for all words in your vocabulary so 250k nodes. The function that does this in the code is init_node_feats. The number of words for the datasets in the repo can be see in data/corpus
and looking at the {dataset}_vocab.txt
files. For example, for the r8_presplit dataset there are 7688 nodes. This vocabulary is built from the sentence and words of frequency greater than 5 are kept. See the build_text_graph_dataset function. However, it must also be noted that 250k nodes with one hot initialization would not be feasible as each node would have a initial feature dimension of 250k and the graph would not be able to be loaded in system memory.
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lets assume i have 5000 documents and their 5000 integer labels and in this corpus we got 14000 unique words.
according to paper total num of nodes will be ==> total documents + vocab size = 5000+14000= 19000 nodes
but for documents we know the labels ,how are you creating the labels for vocab word (nodes)
can u clarify on this
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Related Issues (11)
- Building graph HOT 1
- IndexError
- Error in model_text_gnn.py when using the expand method HOT 2
- AttributeError: 'int' object has no attribute 'numel' HOT 6
- RuntimeError: Detected that PyTorch and torch_sparse were compiled with different CUDA versions. PyTorch has CUDA version 10.1 and torch_sparse has CUDA version 11.0. Please reinstall the torch_sparse that matches your PyTorch install
- AssertionError HOT 1
- Label of nodes belonging to vocab words
- tcmalloc: large alloc
- AttributeError: 'int' object has no attribute 'new_full' HOT 3
- How do I get the text map for this build
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