GraphSAGE (original implementation): https://github.com/williamleif/GraphSAGE

This is another GraphSAGE implementation

• Simplifying things for studying purpose
  • MeanAggregator only
  • CPU runtime only
• Python 3
• TensorFlow 2

It requires python3 with both tensorflow and scikit-learn packages.

./supervised.py

./unsupervised.py

1. Computing diffusion matrix and bitmask for node feature and neighbor concatenation outside TensorFlow (in minibatch.py) since this is faster. Tried to write this part inside tensorflow but got 50x slower (branch #internaldiffusion) not using tf.function. It seems too complicated to get rid of python dict and use only matrices and tf.function, which I haven't tested.
2. fit and fit_generator in tf.keras.Model do not support more than one arguments, so I manually write the training loop with tf.GradientTape().

The supervised part is 35% faster than graphsage-simple (PyTorch version of GraphSage)

• cora dataset, minibatch size 256 units, average batch time on a 32-vcore testbed:
• graphsage-simple updated version: 0.03806s
• graphsage-tf2 (this one): 0.02408s

This unsupervised part is much slower than GraphSage original implementation and cannot parallelize well with multi-core CPU. It is under investigation.