Graph Neural Network - Edge Features Prediction

Dear all,
I would have a question about prediction of Edge Features through Tensorflow GNN, in particular I am Running TF-GNN 1.0.0 under TensorFlow 2.15.0.

As I get to the definition of the Task of the Runner, I can only work on nodes or features, through
runner.GraphMeanSquaredError or runner.NodeMeanSquaredError.

How can I let my runner handle edge features?

Thanks in advance for the support,

1 Like

Deep Learning with TensorFlow [Community Edition]

In TensorFlow Graph Neural Networks (TF-GNN), if you’re looking to predict edge features, you might need to define a custom task or extend the functionality of the library, since the built-in tasks such as runner.GraphMeanSquaredError or runner.NodeMeanSquaredError are primarily designed for node and graph-level predictions.

To work with edge features, you typically need to implement a model that can handle edge inputs and produce edge outputs. This often involves defining a custom layer or model that computes representations for edges based on the features of the nodes they connect and potentially the features of the edges themselves.

Here’s a general approach to predicting edge features with TF-GNN:

  1. Define Edge Representation: Start by defining how you want to represent your edges. This could involve using the features of the nodes connected by the edge and optionally, any existing features of the edges themselves.
  2. Custom Edge Layer: Implement a custom layer that takes node features and edge features (if available) as inputs and produces updated edge features. This layer can use operations such as concatenating the features of the source and target nodes, applying a transformation (e.g., a neural network), and then combining these with the original edge features.
  3. Model Architecture: Integrate this custom edge layer into your GNN model architecture. Ensure that your model processes node features through the GNN layers and then passes the resulting node representations, along with any original edge features, to your custom edge layer to produce the predicted edge features.
  4. Loss Function and Task Definition: Define a loss function that is appropriate for your edge feature prediction task. This could be mean squared error if your edge features are continuous or cross-entropy loss if they are categorical. You will need to implement a custom training loop or task in TF-GNN that computes this loss based on the predicted and true edge features.
  5. Training: During training, ensure that your data includes the true edge features as targets for the predictions. Your custom training loop or task should compute the loss between the predicted edge features from your model and these true edge features and update the model parameters based on this loss.
  6. Evaluation: Similarly, for evaluation, you will compute metrics relevant to your task (e.g., MSE for regression tasks) based on the predicted and true edge features.

Because implementing this functionality might require diving deep into the TF-GNN library and TensorFlow itself, you should be comfortable with customizing TensorFlow models and potentially contributing to or modifying the TF-GNN library. If TF-GNN’s documentation or community forums provide any examples or guidance on edge feature prediction, those resources could be incredibly valuable as you develop your solution.