Summary of Classifying Nodes in Graphs Without Gnns, by Daniel Winter et al.
Classifying Nodes in Graphs without GNNs
by Daniel Winter, Niv Cohen, Yedid Hoshen
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a novel approach to node classification in graphs that eliminates the need for graph neural networks (GNNs) during both training and testing. The authors analyze the role of GNNs in distillation methods and identify limitations that can be addressed by developing a fully GNN-free method. The new approach consists of three components: smoothness constraints, pseudo-labeling iterations, and neighborhood-label histograms. Experimental results on popular benchmarks, including citation and co-purchase networks, demonstrate state-of-the-art accuracy without requiring the use of GNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better classify nodes in graphs without using special computer programs called graph neural networks (GNNs). Right now, we need GNNs during training and testing, but this can be a problem. The authors studied why we need GNNs and found ways to fix the issues. They created a new way of classifying nodes that doesn’t require GNNs at all! This method has three main parts: making sure the labels are smooth, using fake labels to help, and looking at what’s around each node. The results show that this new approach works just as well as other methods on famous datasets. |
Keywords
* Artificial intelligence * Classification * Distillation * Gnn
