Summary of Kagnns: Kolmogorov-arnold Networks Meet Graph Learning, by Roman Bresson and Giannis Nikolentzos and George Panagopoulos and Michail Chatzianastasis and Jun Pang and Michalis Vazirgiannis
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
by Roman Bresson, Giannis Nikolentzos, George Panagopoulos, Michail Chatzianastasis, Jun Pang, Michalis Vazirgiannis
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper explores the performance of Kolmogorov-Arnold Networks (KANs) compared to Multi-Layer Perceptrons (MLPs) in Graph Neural Networks (GNNs). It introduces three new KAN-based GNN layers inspired by GCN, GAT, and GIN, and evaluates their performance on node classification, link prediction, graph classification, and graph regression tasks. The results show that KANs are comparable to or better than MLPs in all tasks, with the added benefit of being viable alternatives due to similar size and training speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares two different types of neural networks, Multi-Layer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs), for use in Graph Neural Networks. It shows that KANs can be just as good or even better than MLPs at tasks like classifying nodes on a graph or predicting links between them. The paper also compares the size and speed of these networks, showing that they are similar to MLPs. |
Keywords
* Artificial intelligence * Classification * Gcn * Gnn * Regression
