[论文解读] Graph Normalizing Flows

Jenny Liu, Aviral Kumar+3 more|arXiv (Cornell University)|May 30, 2019

Advanced Graph Neural Networks参考文献 30被引用 39

一句话总结

所提供的文本是 NeurIPS 的格式化说明，而非 Graph Normalizing Flows 的实际论文内容。

ABSTRACT

We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the unsupervised case, we combine graph normalizing flows with a novel graph auto-encoder to create a generative model of graph structures. Our model is permutation-invariant, generating entire graphs with a single feed-forward pass, and achieves competitive results with the state-of-the art auto-regressive models, while being better suited to parallel computing architectures.

研究动机与目标

• 输入不包含论文的目标；它似乎是 NeurIPS 2019 的格式化指南，而不是 Graph Normalizing Flows 的内容。

提出的方法

• 在提供的文本中没有方法论细节；它包含了用于 NeurIPS 投稿的格式规则和示例。

实验结果

研究问题

• RQ1 在所提供的文本中没有给出研究问题。

主要发现

• 在所提供的文本中没有给出发现。