Sure! Here is a tutorial on how to use the GNN Node Sampler in JAX with HTML tags:
GNN Node Sampler in JAX Tutorial
In this tutorial, we will learn how to use the GNN Node Sampler in JAX to sample nodes from a graph for training Graph Neural Networks (GNNs).
Step 1: Install JAX and Graph Neural Networks (GNNs) libraries
First, you need to install the JAX and GNN libraries. You can do this using pip:
$ pip install jax jaxlib jaxlib-cpu jaxlib-gpu
$ pip install jax GNN-libraries
Step 2: Sample Nodes from a Graph using the GNN Node Sampler
Now that you have the required libraries installed, let’s create a simple example to sample nodes from a graph using the GNN Node Sampler:
import jax
import jax.numpy as jnp
from gnn_libraries import GNNNodeSampler
# create a graph adjacency matrix
adj_matrix = jnp.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])
# instantiate the GNN Node Sampler
node_sampler = GNNNodeSampler(adj_matrix)
# sample nodes from the graph
sampled_nodes = node_sampler.sample_nodes(2)
print("Sampled nodes:", sampled_nodes)
In this example, we first create a graph adjacency matrix representing the connections between nodes in the graph. We then create an instance of the GNN Node Sampler class passing in the adjacency matrix. Finally, we call the sample_nodes method to sample 2 nodes from the graph and print the result.
Conclusion
Congratulations! You have learned how to use the GNN Node Sampler in JAX to sample nodes from a graph for training Graph Neural Networks. Feel free to experiment with different graph structures and sampling strategies to further enhance your GNN models.
Mistakes in the video (the ones that I know about 😅):
– I changed the implementation for the neighbor_sampler function. It previously takes one minute to sample from Cora dataset. Now, it samples in around 10 seconds. That’s because I removed the unique function and used sparse matrices indexing. The link in the description has the new code.
– In 32:54 I said that the probability of picking an edge is 1. That's actually not true, it's 1/|E|, where |E| is the number of edges in the graph. I fixed it on my github notes.