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[1905.13177] Graph Normalizing Flows
Source: arxiv.org
Article summary:
1. Graph Normalizing Flows is a new, reversible graph neural network model for prediction and generation.
2. It has a significantly reduced memory footprint compared to message passing neural networks, allowing it to scale to larger graphs.
3. It combines with a novel graph auto-encoder to create a generative model of graph structures that is permutation-invariant and can generate entire graphs with a single feed-forward pass.
Article analysis:
The article appears to be reliable and trustworthy as it provides detailed information about the Graph Normalizing Flows model, its advantages over existing models, and its potential applications in supervised and unsupervised tasks. The authors provide evidence for their claims by citing relevant research papers and experiments conducted using the model. Furthermore, the article does not appear to be biased or one-sided as it presents both sides of the argument equally. Additionally, there are no unsupported claims or missing points of consideration in the article. However, it should be noted that the article does not explore any counterarguments or possible risks associated with using this model which could be explored further in future research.