Summary of Revisiting the Necessity Of Graph Learning and Common Graph Benchmarks, by Isay Katsman et al.
Revisiting the Necessity of Graph Learning and Common Graph Benchmarks
by Isay Katsman, Ethan Lou, Anna Gilbert
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A recent paper in the field of graph machine learning challenges the common assumption that node features alone are insufficient for graph learning tasks. The authors demonstrate that, surprisingly, node features can be more-than-sufficient for many common graph benchmarks, rendering the use of graph structure unnecessary or even redundant. This finding is supported by a feature study on seven commonly used graph learning datasets, which shows that the features themselves contain enough graph information to obviate the need for graph-based methods. The authors also introduce a challenging parametric family of synthetic datasets that require graph information for non-trivial performance and identify a subset of real-world datasets that are not trivially solved by an MLP, making them suitable benchmarks for graph neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are everywhere in big industries! Scientists have been using special computer programs to learn from these graphs. But they always assume that the program needs to look at how the different pieces of information (called nodes) are connected. New research says this might not be true! It turns out that sometimes, just looking at what’s inside each node is enough to get good results. This can save time and computers! The researchers tested their idea on many common datasets and found it works for most of them. They also created new fake datasets that need graph information to solve correctly and some real-world datasets that are challenging even for the best programs. |
Keywords
» Artificial intelligence » Machine learning
