Summary of Probability Passing For Graph Neural Networks: Graph Structure and Representations Joint Learning, by Ziyan Wang et al.
Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning
by Ziyan Wang, Yaxuan He, Bin Liu
First submitted to arxiv on: 15 Jul 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 In this paper, researchers propose a novel approach called Probability Passing to refine the generated graph structure in Graph Neural Networks (GNNs) when analyzing non-Euclidean data. The existing methods neglect noise from node features, which affects the performance and accuracy of GNNs. To address this issue, they introduce a novel method that aggregates edge probabilities of neighboring nodes based on observed graph structures. This approach is combined with Latent Graph Inference (LGI) to produce predictions. The authors also employ an anchor-based technique to reduce complexity and improve efficiency. Experimental results demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how we can use machine learning models like Graph Neural Networks to analyze complex data that isn’t organized in a straightforward way. Right now, these models rely on knowing what the underlying structure of this data is, but they often struggle when the data is noisy or incomplete. To solve this problem, the authors propose a new approach called Probability Passing that refines the generated graph structure by considering the relationships between different parts of the data. They also combine this with another technique called Latent Graph Inference to make their model more accurate and efficient. |
Keywords
» Artificial intelligence » Inference » Machine learning » Probability
