Summary of Anomaly Resilient Temporal Qos Prediction Using Hypergraph Convoluted Transformer Network, by Suraj Kumar et al.
Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network
by Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning architecture called Hypergraph Convoluted Transformer Network (HCTN) is introduced in this paper, designed to tackle quality-of-service (QoS) prediction challenges. The framework addresses data sparsity, cold-start issues, and credibility problems by leveraging diverse features like domain-specific knowledge and high-order patterns. HCTN combines a hypergraph structure with graph convolution over hyper-edges to capture complex correlations, and utilizes transformer networks for dynamic pattern recognition. A sparsity-resilient solution is also included to detect and incorporate the characteristics of greysheep users and services. The model demonstrates state-of-the-art performance on large-scale WSDREAM-2 datasets for response time and throughput. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a system that can predict how well a service will perform over time, taking into account things like changes in network quality and user preferences. This is important because it allows us to make better recommendations to users. The problem is that current methods don’t always work well when there’s not enough data or when the data isn’t reliable. In this paper, scientists introduce a new way of doing QoS prediction that addresses these challenges. It uses a special type of artificial intelligence called a deep learning model, which can learn patterns in large amounts of data. The model is able to capture complex relationships between different factors that affect service performance. |
Keywords
» Artificial intelligence » Deep learning » Pattern recognition » Transformer
