Summary of Graph Community Augmentation with Gmm-based Modeling in Latent Space, by Shintaro Fukushima et al.
Graph Community Augmentation with GMM-based Modeling in Latent Space
by Shintaro Fukushima, Kenji Yamanishi
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); 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 paper presents a novel approach to graph generation using generative models, specifically addressing the problem of graph community augmentation. The authors aim to generate unseen or unfamiliar graphs with a new community, which can be beneficial in social networks or data mining applications where collecting real graph data is challenging. To achieve this, they propose an algorithm called Graph Community Augmentation (GCA), which involves fitting Gaussian mixture models (GMM) to the latent space of nodes and adding new clusters based on the minimum description length (MDL) principle. The effectiveness of GCA is demonstrated empirically on synthetic and real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores a way to create new graphs with different communities using generative models. This can be helpful in social networks or data mining, where it’s hard to get enough real graph data. To do this, the authors created an algorithm called Graph Community Augmentation (GCA). GCA works by fitting special models (Gaussian mixture models) to the space where nodes are embedded and then adding new points based on a principle that makes the descriptions shorter. The results show that GCA can effectively generate graphs with new community structures. |
Keywords
» Artificial intelligence » Latent space
