Summary of Gkan: Graph Kolmogorov-arnold Networks, by Mehrdad Kiamari et al.
GKAN: Graph Kolmogorov-Arnold Networks
by Mehrdad Kiamari, Mohammad Kiamari, Bhaskar Krishnamachari
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We introduce Graph Kolmogorov-Arnold Networks (GKAN), an innovative neural network architecture that extends the principles of Kolmogorov-Arnold Networks (KAN) to graph-structured data. Unlike traditional Graph Convolutional Networks (GCNs), GKANs implement learnable spline-based functions between layers, transforming information processing across graphs. We present two architectures: architecture 1 applies learnable functions after aggregation and architecture 2 applies them before aggregation. Empirically evaluating GKAN on a semi-supervised graph learning task using the Cora dataset, we find that architecture generally performs better. Compared to GCN, GKAN achieves higher accuracy in semi-supervised learning tasks. For example, with 100 features, GCN provides an accuracy of 53.5 while GKAN achieves 61.76; with 200 features, GCN provides an accuracy of 61.24 while GKAN achieves 67.66. We also explore the impact of various parameters on GKAN performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re introducing a new way to analyze complex data that’s connected like a web. It’s called Graph Kolmogorov-Arnold Networks (GKAN). This new method is different from what we currently use because it can learn and adapt as it goes, rather than just following strict rules. We tested this method on real-world data and found that it performs better than the traditional way of doing things. Specifically, when we used GKAN to analyze a dataset called Cora, we got more accurate results compared to the traditional method. |
Keywords
» Artificial intelligence » Gcn » Neural network » Semi supervised
