Edge dynamics in complex networks for data classification

• Grant number: 15/18456-6
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: February 01, 2016
• End date: January 29, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Zhao Liang
• Grantee: Filipe Alves Neto Verri
• Supervisor: Ying-Cheng Lai
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: Arizona State University, Tempe (ASU), United States
• Associated to the scholarship: 13/25876-6 - High level data classification based on complex network applied to invariant pattern recognition, BP.DD

Abstract

Complex networks are graphs that model natural systems with nontrivial topology and dynamics. The topological properties and the evolution of these networks give information about the systems that they represent. This theory may be applied to solve machine learning tasks such as data classification. One example of dynamical learning mechanism in complex networks is the particle competition. Recently, an alternative particle competition scheme emphasizing the edge dynamics was proposed. This approach, called Edge Domination System, offers better understanding of the system behavior than the vertex dynamics. The objectives of the regular fellowship are developing, analyzing, and applying data classification techniques based on dynamical systems in complex networks. The internship abroad intends to enhance the mathematical background that is fundamental to investigate such systems. This research plan proposes to study, analyze and model dynamical systems emphasizing edge dynamics for data classification problems. (AU)

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

NETO VERRI, FILIPE ALVES; URIO, PAULO ROBERTO; ZHAO, LIANG. Network Unfolding Map by Vertex-Edge Dynamics Modeling. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v. 29, n. 2, p. 405-418, FEB 2018. (13/07375-0, 13/25876-6, 15/18456-6, 11/50151-0)