Summary of Generalizing Weisfeiler-lehman Kernels to Subgraphs, by Dongkwan Kim et al.
Generalizing Weisfeiler-Lehman Kernels to Subgraphs
by Dongkwan Kim, Alice Oh
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 Subgraph representation learning has been a crucial component in solving various real-world problems. However, current graph neural networks (GNNs) have limitations when it comes to capturing complex interactions within and between subgraphs for subgraph-level tasks. The proposed WLKS model addresses this issue by applying the Weisfeiler-Lehman algorithm on induced k-hop neighborhoods, combining kernels across different k-hop levels to capture richer structural information. This approach eliminates the need for neighborhood sampling, providing a more expressive and efficient alternative. In experiments on eight real-world and synthetic benchmarks, WLKS significantly outperforms leading approaches on five datasets while reducing training time by up to 0.25x compared to the state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand how different parts of a big graph are connected. Graph neural networks (GNNs) have been very good at this, but they still struggle when it comes to understanding smaller groups within the bigger graph. This paper proposes a new way to learn about these small groups by looking at the connections between them. The new method is called WLKS and it’s better than existing methods in many cases. It can even be faster and more efficient! In tests on different kinds of graphs, the new method performed well on five out of eight datasets. |
Keywords
» Artificial intelligence » Representation learning
