Summary of Almost Surely Asymptotically Constant Graph Neural Networks, by Sam Adam-day et al.
Almost Surely Asymptotically Constant Graph Neural Networks
by Sam Adam-Day, Michael Benedikt, İsmail İlkan Ceylan, Ben Finkelshtein
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic in Computer Science (cs.LO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 expressive power of graph neural networks (GNNs) by analyzing how their predictions evolve on larger graphs. It shows that the output converges to a constant function, which limits what these classifiers can uniformly express. This phenomenon applies to various GNNs, including state-of-the-art models with different aggregation mechanisms. The results are validated through empirical experiments on random and real-world graphs, highlighting the importance of understanding the expressive capabilities of GNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict things about a graph, like who is friends with whom. Graph neural networks (GNNs) can do this well, but how good are they really? The researchers in this paper found that as they used these predictions on bigger and more complex graphs, the results started to get stuck and couldn’t get any better. This means that there are limits to what GNNs can do, even with advanced models like attention-based graph transformers. They tested their findings on both random and real-world graphs, showing that this “limit” applies to many types of graphs. |
Keywords
* Artificial intelligence * Attention
