Summary of Graph Elimination Networks, by Shuo Wang et al.
Graph Elimination Networks
by Shuo Wang, Ge Cheng, Yun Zhang
First submitted to arxiv on: 2 Jan 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 This paper explores the limitations of Graph Neural Networks (GNNs) in deep layers, where they typically underperform due to ineffective neighborhood feature propagation. Unlike previous research attributing this issue to node over-smoothing, the authors pinpoint the root cause as an exponential growth of a node’s current representation at every propagation step, making it challenging to capture dependencies between distant nodes. To address this problem, the authors introduce Graph Elimination Networks (GENs), which employ an algorithm to eliminate redundancies during neighborhood propagation. GENs are shown to enhance nodes’ perception of distant neighborhoods and extend the depth of network propagation, outperforming state-of-the-art methods on various graph-level and node-level datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are super smart machines that can learn from graphs, but they have a big problem: they get worse as you go deeper. People thought it was because the nodes got too similar, but nope! It’s actually because the way they share information with each other gets really bad. The authors of this paper figured out what’s going wrong and came up with a new solution called Graph Elimination Networks (GENs). GENs are like special filters that help GNNs learn better from their neighbors, even when they’re really far away. It makes them way more powerful and helps them do tasks better! |
