GraphSAGE
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Representation learning on large graphs using stochastic graph convolutions.
if i want to change the dataset, how ?
IOError: [Errno 2] No such file or directory: './example_data/ppi-G.json' root@9351d38c244b:/notebooks# sh example_unsupervised.sh Loading training data.. Traceback (most recent call last): File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main "__main__", fname, loader, pkg_name)...
hello I get a new dataset,and I want to process it into a form that can be used for this project,but I don't know what this class specifically refers to.Can...
The code is perfact when I use the tensorflow1.8, but I want try to use GPU. I try the different tensorflow_gpu version(1.8 and 1.15), but always tips me the BLAS...
This might be a dumb question, but could somebody kindly tell me the difference between `features in -G.json` and` features in -feats.npy`?
I tried to convert the code into tensorflow 2.X (2.10), wanted to run reddit dataset, since networkx 1.11 is not compatible with TF 2.X, getting following error Removed 231443 nodes...
the line 56 and 57 of utils.py ``` if (G.node[edge[0]]['val'] or G.node[edge[1]]['val'] or G.node[edge[0]]['test'] or G.node[edge[1]]['test']): ``` may be ``` if (G.nodes[edge[0]]['val'] or G.nodes[edge[1]]['val'] or G.nodes[edge[0]]['test'] or G.nodes[edge[1]]['test']): ``` ?
It is known that He initialization works better with ReLU since Xavier kill half of the input. So, in `layers.py` ``` self.vars['weights'] = tf.get_variable('weights', shape=(input_dim, output_dim), dtype=tf.float32, initializer=tf.contrib.layers.variance_scaling_initializer(), regularizer=tf.contrib.layers.l2_regularizer(FLAGS.weight_decay)) ```...
I'm running the command and got the error ` python -m graphsage.supervised_train --train_prefix toy-ppi ./example_data/ppi --model graphsage_mean --sigmoid` Loading training data.. **Traceback (most recent call last): File "/usr/lib/python2.7/runpy.py", line 174,...
Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 1.8.0 to 2.9.3. Release notes Sourced from tensorflow's releases. TensorFlow 2.9.3 Release 2.9.3 This release introduces several vulnerability fixes: Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887) Fixes a...