Summary of An Adaptive Latent Factorization Of Tensors Model For Embedding Dynamic Communication Network, by Xin Liao et al.
An Adaptive Latent Factorization of Tensors Model for Embedding Dynamic Communication Network
by Xin Liao, Qicong Hu, Peng Tang
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 Dynamic Communication Network (DCN) is a crucial data source for big-data applications, representing interactions between nodes over time. As the number of nodes increases, the DCN can be represented as a High-Dimensional Sparse (HDS) tensor. To extract rich behavioral patterns from this type of tensor, an Adaptive Temporal-dependent Tensor low-rank representation (ATT) model is proposed. The ATT model employs a three-fold approach: designing a temporal-dependent method to reconstruct temporal feature matrices, adapting hyper-parameters using Differential Evolutionary Algorithms (DEA), and employing nonnegative learning schemes for the model parameters. Experimental results on four real-world DCNs show that the proposed ATT model outperforms state-of-the-art models in both prediction errors and convergence rounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Dynamic Communication Network is a way to look at how different things communicate with each other over time. It’s important for big-data applications, like understanding how people talk to each other online or how companies share information. As the number of things that are communicating grows, it can be represented as a special kind of mathematical object called a High-Dimensional Sparse tensor. To get useful insights from this type of data, researchers developed a new model called Adaptive Temporal-dependent Tensor low-rank representation (ATT). This model helps by reconstructing patterns in time, adapting to different settings, and using special rules to make sure the results are accurate. |
