Multilayer Complex Network-Based Machine Learning for Heterogeneous Dynamical Data Representation and Processing of Soybean Data

• Grant number: 25/13510-4
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: March 01, 2026
• End date: February 29, 2028
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Zhao Liang
• Grantee: Murtiza Ali
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Associated research grant: 24/15430-5 - Machine learning-based multi-modal data fusion and growth modeling for soybean production improvement, AP.PFPMCG.TEM

Abstract

Complex networks provide big advantages for data representation, allowing to capture spatial, topological, dynamical and functional relations of large data sets. This project has objective to design a general scheme for heterogeneousdynamical data representation and analysis. Specifically, the input data will be represented by multilayer complex networks, then, community detection techniques in multilayer networks will be developed. After that, the community detection method and relevant multilayer network measures are used to characterize the relationship amongdifferent parts of the network in different scales, with objective to find out new knowledge. The network-based data representation scheme will be applied to integrate various types of soybean data, with the objectives of predicting production and uncovering cause-effect relationships or other types of correlations.