Summary of Sharp Bounds For Poly-gnns and the Effect Of Graph Noise, by Luciano Vinas and Arash A. Amini
Sharp Bounds for Poly-GNNs and the Effect of Graph Noise
by Luciano Vinas, Arash A. Amini
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 The paper investigates the performance of graph neural networks with graph-polynomial features (poly-GNNs) for semi-supervised node classification under a general contextual stochastic block model (CSBM). It analyzes poly-GNNs’ output node representations and shows that the rate of separation between classes does not depend on the network’s depth, negating any benefit from further aggregation. The study highlights the impact of “graph noise” in deep GNNs and demonstrates how graph structure noise can dominate other sources of signal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how well graph neural networks (GNNs) with special features do at classifying nodes on graphs when some information is missing. They look at what happens when these networks get deeper, thinking that more layers might help them separate different groups better. But they found out that having many layers doesn’t really make a difference – even the shallowest network does just as well! They also showed how extra noise in the graph can mess things up and make it harder to learn from. |
Keywords
» Artificial intelligence » Classification » Semi supervised
