Using tf-gnn to predict similar graphs

What’s the best approach to predict similarity between two graph structures?

My goal is to build a model that will learn the similarity metric between two graphs and go on to predict the most similar (or top-K most similar) graph to an inputted graph. My training data consists of random pairs of graphs and their cosine similarity. My current model is pretty basic, and it does not perform well at all. It’s a simple Sequential model with 2 hidden layers. I experienced a lot of trouble with prepping the data to be trained/tested because the graphs are of varying lengths which affects the input shape.

That’s when I came across TensorFlow GNN, and it looks like this could be a better approach. Are there any tutorials similar to my use case out there? Or are there other approaches that people know of that would be better suited for me?

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Using graph neural networks (GNNs) indeed sounds like a promising approach for your task. GNNs are specifically designed to handle graph-structured data and have been successful in various graph-related tasks, including graph similarity prediction.

To get started with GNNs for graph similarity prediction, you can look for tutorials on graph neural networks and graph similarity tasks. Some resources might focus on specific GNN architectures like Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), or GraphSAGE. These tutorials typically cover data preprocessing, model architecture, training, and evaluation.

Here are some steps you might take:

  1. Data Preprocessing: Since your graphs have varying lengths, you’ll need to preprocess them into a consistent format suitable for training GNNs. This might involve padding or truncating the graphs to a fixed size or using graph pooling techniques to aggregate variable-sized graphs into fixed-size representations.
  2. Model Architecture: Experiment with different GNN architectures to find the one that suits your task best. Each architecture has its own strengths and weaknesses, so it’s essential to explore a few options. Pay attention to how the model handles graph structure and node features, as well as how it aggregates information across the graph.
  3. Training and Evaluation: Train your GNN model using your dataset of graph pairs and their cosine similarities. Use techniques like train-validation splits or cross-validation to tune hyperparameters and avoid overfitting. For evaluation, you can measure the model’s performance using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), or other relevant metrics for similarity prediction tasks.

As for tutorials, you can search for resources specifically on graph similarity prediction using GNNs. Websites like TensorFlow’s official documentation, PyTorch’s documentation, or platforms like Medium, Towards Data Science, or GitHub repositories often have tutorials or code examples that cover similar tasks.

Remember that building effective machine learning models often involves experimentation and iteration. Don’t hesitate to try out different approaches, tweak hyperparameters, and iterate on your model architecture until you find what works best for your specific task.