Summary of Scalable Weibull Graph Attention Autoencoder For Modeling Document Networks, by Chaojie Wang et al.
Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks
by Chaojie Wang, Xinyang Liu, Dongsheng Wang, Hao Zhang, Bo Chen, Mingyuan Zhou
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed Graph Poisson Factor Analysis (GPFA) and its extension, the Graph Poisson Gamma Belief Network (GPGBN), improve upon traditional Relational Topic Models (RTMs) by providing analytic conditional posteriors for more accurate inference. This paper combines GPGBN with Weibull-based graph inference networks to create two variants of Weibull Graph Auto-Encoder (WGAE), which are equipped with model inference algorithms. The authors demonstrate that their models can extract high-quality hierarchical latent document representations and achieve promising performance on various graph analytic tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops new models for analyzing a collection of interconnected documents, called relational topic models. These models help understand both link structures and document contents. The authors combine these models with variational graph autoencoders to create new models that can better capture the complex relationships between documents. The results show that their models are very good at finding hidden patterns in the data and making predictions. |
Keywords
» Artificial intelligence » Encoder » Inference
