A Gentle Introduction to Graph Neural Networks
Graph Neural Networks (GNNs) process graph-structured data and have applications in fields like drug discovery and social network analysis, with tasks categorized into graph-level, node-level, and edge-level predictions.
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Graph Neural Networks (GNNs) are a specialized type of neural network designed to process data structured as graphs, which consist of nodes (entities) and edges (relationships). This article provides a comprehensive overview of GNNs, detailing their components, design choices, and practical applications across various fields such as drug discovery, physics simulations, and social network analysis. The discussion is divided into four main sections: the nature of graph data, the unique characteristics of graphs compared to other data types, the construction of a modern GNN model, and an interactive GNN playground for hands-on learning. Graphs can represent diverse data types, including images and text, by treating pixels or words as nodes connected by edges. The article also categorizes prediction tasks into graph-level, node-level, and edge-level, illustrating how GNNs can be applied to solve these problems. Examples include predicting molecular properties, classifying social network members, and understanding relationships in visual scenes. The flexibility of graphs allows for a wide range of applications, making GNNs a powerful tool in machine learning.
- Graph Neural Networks (GNNs) are designed to process graph-structured data.
- GNNs have applications in various fields, including drug discovery and social network analysis.
- The article categorizes prediction tasks into graph-level, node-level, and edge-level.
- GNNs can represent diverse data types, such as images and text, as graphs.
- An interactive GNN playground is provided for practical learning and experimentation.
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- GNNs are seen as having potential in various fields, including physics simulations, but face challenges in generalization across different graph structures.
- There is a sentiment that GNNs have not lived up to expectations compared to Convolutional Neural Networks (CNNs), particularly in terms of performance and flexibility.
- The lack of datasets for GNNs is noted as a barrier to their wider adoption and discussion.
- Some commenters express disappointment with the current state of GNNs, suggesting that attention mechanisms and transformers may be overshadowing them.
- There is curiosity about the ability of GNNs to adapt to changing topologies and the potential for new breakthroughs in this area.
13 commentsIn practice, every such mesh/graph is used once to solve a particular problem. Hence it makes little sense to train a GNN for a specific graph. However, that's exactly what most papers did because no one found a way to make a GNN that can adjust well to a different mesh/graph and different simulation parameters. I wonder if there's a breakthrough waiting just around the corner to make such a generalization possible.
On GNN's, the lack of datasets [2] might be a reason they are not as talked about. This is something that has affected also the semantic web domain.
[1] https://distill.pub/2021/distill-hiatus/
[2] https://huggingface.co/datasets?task_categories=task_categor...
For a long time GNNs were pitched as a generalization of CNNs. But CNNs are more powerful because the "adjacency weights" (so to speak) are more meaningful: they learn relative positional relationships. GNNs usually resort to pooling, like described here. And you can output an image with a CNN. Good luck getting a GNN to output a graph. Topology still has to be decided up front, sometimes even during training. And the nail in the coffin is performance. It is incredible how slow GNNs are compared to CNNs.
These days I feel like attention has kinda eclipsed GNNs for a lot of those reasons. You can make GNNs that use attention instead of pooling, but there isn't much point. The graph is usually only traversed in order to create the mask matrix (ie attend between nth neighbors) and otherwise you are using a regular old transformer. Often you don't even need the graph adjacencies because some kind of distance metric is already available.
I'm sure GNNs are extremely useful to someone somewhere but my experience has been a hammer looking for a nail.
What is the generalised formula for calculating this, given the number of nodes but also edges need to be considered.
It doesn't appear to be explained in the article. I think it may be a factorial?
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