Summary Graph Neural Networks for Non-Informative Graph Structures arxiv.org
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This study investigates the behavior of Graph Neural Networks in cases of overfitting non-informative graph structures for outcome prediction.
Slides
Slide Presentation (11 slides)
Key Points
- Graph Neural Networks (GNNs) may overfit non-informative graph structures.
- GNNs trained on regular graphs converge to more robust solutions.
- The behavior of GNNs on non-informative graph structures is examined.
- Homogeneous neural networks trained with gradient descent on linearly separable data converge to the max-margin solution.
- The R-COV method improves the performance of GNNs on non-informative graphs.
- Various scientific papers related to GNNs and graph structures are referenced.
- The implicit bias of GNNs is discussed, along with a theorem proof and extensions.
- Different datasets, such as NCI1 and COLLAB, are used to evaluate GNN performance.
Summaries
27 word summary
Graph Neural Networks (GNNs) are often used for outcome prediction, but there is concern about overfitting non-informative graph structures. This study explores GNN behavior in such cases.
34 word summary
Graph Neural Networks (GNNs) are commonly used for predicting outcomes in various domains. However, there is a concern that GNNs may overfit non-informative graph structures. This study investigates the behavior of GNNs on non-in
428 word summary
Graph Neural Networks (GNNs) are commonly used for predicting outcomes in various domains. However, there is a concern that GNNs may overfit non-informative graph structures, using them even when they should be ignored. This study investigates
Graph Neural Networks (GNNs) trained on regular graphs converge to unique solutions that are more robust to graph-structure overfitting. An extrapolation result and assessment for GNNs trained on regular graphs is established, incorporating insights from the implicit
The study examines the behavior of Graph Neural Networks (GNNs) on non-informative graph structures. The experiments involve training GNNs on empty graphs and comparing their performance to GNNs trained on non-empty graphs. A fixed architecture is
A theorem is presented stating that homogeneous neural networks trained with gradient descent on linearly separable data converge to the max-margin solution. This theorem is then applied to graph neural networks (GNNs) trained on regular graphs, showing that gradient descent conver
The method described in the document aims to make given graphs more similar to regular graphs by reducing their coefficient of variation (COV), which measures the variability of node degrees. The authors achieve this by adding edges randomly between nodes of low degree until a certain
When given non-informative graphs, the performance of graph neural networks (GNNs) decreases. However, using the R-COV method significantly improves performance, even with just 3 examples. The Edge and Motif task is not realizable
This text excerpt includes a list of references to various scientific papers related to graph neural networks and related topics. Some of the key papers mentioned include "Emergence of Scaling in Random Networks" by Barabasi and Albert (1999), "SGD
The text excerpt discusses the implicit bias of Graph Neural Networks (GNNs) and presents a theorem proof and extensions. It begins by referencing various papers related to GNNs. The main focus is on analyzing one-layer linear GNNs with no
The excerpt discusses graph neural networks (GNNs) for non-informative graph structures. It introduces equations and conditions related to the max-margin problem and the KKT stationarity condition. It then presents the updates and final predictor for a 2
The study applied a test set consisting of 100 graph examples, each with 20 nodes, to evaluate the performance of Graph Neural Networks (GNNs) on different graph structures. The node features were kept the same across all test sets, while
The document discusses various datasets used in evaluating graph neural networks. The NCI1 dataset focuses on chemical compounds and classifies them based on their ability to suppress or inhibit tumor growth. The COLLAB dataset is a scientific collaboration dataset that represents researchers and their