2020/09/28 by Esteban Wilfredo Vilca Zuñiga, Liang Zhao, Zuñiga, Esteban Wilfredo Vilca +1
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Network Security and Intrusion Detection
paper · pdf · doi:10.48550/arxiv.2009.13511
openalex publication_date 2020/09/28 · openalex created_date 2020/10/01 · openalex updated_date 2026/07/28
High-level classification algorithms focus on the interactions between instances. These produce a new form to evaluate and classify data. In this process, the core is a complex network building methodology. The current methodologies use variations of kNN to produce these graphs. However, these techniques ignore some hidden patterns between attributes and require normalization to be accurate. In this paper, we propose a new methodology for network building based on attribute-attribute interactions that do not require normalization. The current results show us that this approach improves the accuracy of the high-level classification algorithm based on betweenness centrality.