Summary of Transfer Learning For Latent Variable Network Models, by Akhil Jalan et al.
Transfer Learning for Latent Variable Network Models
by Akhil Jalan, Arya Mazumdar, Soumendu Sundar Mukherjee, Purnamrita Sarkar
First submitted to arxiv on: 5 Jun 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 This research paper investigates transfer learning in latent variable network models. The authors aim to estimate the conditional edge probability matrices given latent variables for the target network, using data from both the source network and a subgraph induced by a fraction of the nodes. They show that if the latent variables are shared between the source and target networks, it is possible to achieve vanishing error. The authors propose an efficient algorithm that utilizes graph distance and demonstrate its effectiveness in real-world and simulated graph transfer problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers look at how we can learn about one type of complex network by using information from another similar network. They want to figure out the patterns or connections between things in a new network based on what they know about a similar network. The authors show that if there is some connection or shared information between the two networks, it’s possible to get really good at predicting the patterns and connections in the new network. They also share an algorithm that can do this quickly and accurately. |
Keywords
» Artificial intelligence » Probability » Transfer learning
