Summary of Partially Trained Graph Convolutional Networks Resist Oversmoothing, by Dimitrios Kelesis et al.
Partially Trained Graph Convolutional Networks Resist Oversmoothing
by Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
First submitted to arxiv on: 17 Oct 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 proposed study explores an intriguing phenomenon in Graph Convolutional Networks (GCNs) where untrained GCNs can generate meaningful node embeddings. The researchers investigate the effect of training only a single layer of a GCN while keeping other layers frozen, proposing a theoretical basis for predicting the contribution of these untrained layers to embedding generation. They also find that network width influences the dissimilarity of node embeddings produced after initial node features pass through the untrained model part. Moreover, they establish a connection between partially trained GCNs and oversmoothing, showing that they can reduce it. Theoretical results are experimentally verified, highlighting the benefits of using deep networks resistant to oversmoothing in “cold start” scenarios where there is a lack of feature information for unlabeled nodes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers looked at how Graph Convolutional Networks (GCNs) work when they’re not fully trained. They found that even untrained GCNs can create useful patterns for nodes in the graph. The study also shows that if you train just one layer of a GCN and keep the rest frozen, it makes a big difference in how well the network works. Additionally, the researchers discovered that how wide the network is affects how different the node embeddings are after they pass through the untrained part of the model. This all helps us understand how to use deep networks that don’t get stuck in oversmoothing, which is important when we’re working with data that has a lot of missing information. |
Keywords
» Artificial intelligence » Embedding » Gcn
