I'm currently working on a project where I want to utilize Graph Neural Networks (GNNs) for image classification tasks. However, I'm facing difficulties in understanding how to implement GNNs specifically for classification purposes.

I have explored convolutional neural networks (CNNs) for image classification, but I believe GNNs could provide valuable insights by capturing the relationships between pixels as a graph structure. I would greatly appreciate it if someone could guide me on how to effectively use GNNs for image classification.

Specifically, I would like to gain insights into the following aspects:

  • How can I represent images as graphs for GNN-based classification? What should be the nodes, edges, and features in this context?
  • What are the recommended GNN architectures or models that have been successfully applied to image classification tasks?
  • Are there any code examples, tutorials, or libraries available that can assist me in implementing GNNs for image classification?

I have a decent understanding of deep learning and have worked with CNNs before, but I'm new to GNNs and their application to image classification. Any guidance, code snippets, or reference materials that could help me get started with GNN-based image classification would be highly appreciated. Thank you!


1 Answer 1


GNNs can be used for image classification and in future may prove to be a better approach, but as of now, CNNs are state-of-the-art.
Steps for image classification using GCN:
-> Converting the images to graphs.
-> Feeding them to GNN.
-> Interpretation or visualization of results.

An image can be represented as a graph with each pixel as a node with edges between them capturing similarity between the nodes. There are multiple ways to capture this similarity, one standard or naive approach is to consider that the neighboring pixels/regions have edges between them.
Read this article for more efficient ways.

You can follow this paper to implement an image classifier using GNN.


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