Summary of Functional-edged Network Modeling, by Haijie Xu and Chen Zhang
Functional-Edged Network Modeling
by Haijie Xu, Chen Zhang
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 introduces a novel approach to network modeling by transforming the adjacency matrix into a functional adjacency tensor, incorporating an additional dimension for function representation. The authors use Tucker functional decomposition to analyze the network’s community structure while regularizing the basis matrices to ensure symmetry. To handle irregular observations of the functional edges, they develop a model inference method that solves a tensor completion problem optimized by a Riemann conjugate gradient descent algorithm. The proposed model is evaluated using simulation data and real-world metro system data from Hong Kong and Singapore. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand networks by thinking about functions instead of just connections between things. They turn the normal network graph into something with extra dimensions that show how functions relate to each other. This helps them identify groups within the network that are related in certain ways. To deal with missing information, they use a special technique called tensor completion. The results are tested using pretend data and real-world metro system data from Hong Kong and Singapore. |
Keywords
* Artificial intelligence * Gradient descent * Inference
