Summary Graph Neural Networks for Non-Informative Graph Structures arxiv.org
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The study investigates whether Graph Neural Networks can ignore irrelevant graph structures and proposes solutions to tackle this problem.
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Key Points
- GNNs have become the dominant approach for learning on graph data in various domains.
- It is not clear if GNNs have the ability to ignore non-informative graph structures.
- GNNs tend to overfit graph structures that should be ignored.
- Reduced COV (R-COV) method improves the performance of GNNs on non-informative graphs.
- The implicit bias of GNNs is analyzed and methods to mitigate overfitting are proposed.
Summaries
20 word summary
This study examines whether Graph Neural Networks (GNNs) can disregard non-informative graph structures and suggests techniques to address this issue.
39 word summary
Graph Neural Networks (GNNs) are widely used for learning on graph data, but it is unclear if they can ignore non-informative graph structures. This study analyzes the implicit bias of GNNs trained on regular graphs and proposes methods to
429 word summary
Graph Neural Networks (GNNs) have become the dominant approach for learning on graph data in various domains. However, it is not clear if GNNs have the ability to ignore non-informative graph structures. This study shows that GNN
In this article, the authors analyze the implicit bias of Graph Neural Networks (GNNs) trained on regular graphs and propose methods to mitigate overfitting of graph structures. They show that GNNs tend to overfit graph structures that should be
In this study on graph neural networks (GNNs), the authors investigate the effect of graph structure on overfitting. They conduct experiments using different graph distributions and compare the performance of GNNs trained on empty graphs versus non-empty graphs. The
Homogeneous neural networks trained with gradient descent on linearly separable data converge to the max-margin solution. This translates to graph neural networks (GNNs) trained on regular graphs converging to the max-margin solution of a quadratic problem. The root
Reduced COV (R-COV) is a method that makes graphs more similar to regular graphs by reducing their coefficient of variation (COV). This is done by adding edges sampled randomly between nodes of low degree, until a certain threshold is exceeded
When given a non-informative graph, the performance of graph neural networks (GNNs) decreases. However, the introduction of R-COV significantly improves performance, even with just three examples. The performance of GNNs trained on informative and
This text excerpt consists of a list of references to various research papers and articles. The references cover a range of topics related to graph neural networks, machine learning, and data analysis. Some of the key points mentioned include the emergence of scaling in random networks
The excerpt discusses the implicit bias of Graph Neural Networks (GNNs) and presents a theorem proof and extensions. It begins by assuming a dataset with graph examples and analyzing one-layer linear GNNs with no readout. The final representation of a
The excerpt discusses graph neural networks (GNNs) for non-informative graph structures. It presents equations and conditions related to the max-margin problem and KKT stationarity condition. The updates and final predictor in a 2-layer GNN are
We applied our method to a test set of 100 graph examples, each with 20 nodes. The test sets have the same node features but different graph structures drawn from different distributions. We computed a ratio for each example and observed that when the example
The document discusses various datasets used in evaluating graph neural networks. The datasets include chemical compound datasets such as PROTEINS, ENZYMES, NCI1, and DD, which are used to predict enzyme classification and tumor growth inhibition. COLLAB