Complex network component unfolding using a particle competition technique

Abstract

I convey my heartfelt gratitude to all the people who have helped and inspired me during my graduate studies at ICMC-USP. I would like to thank my professor, Dr. Zhao Liang, for his insights, advice, patience, dedication, and for keeping me on the line while extending, at the same time, freedom to drive decisions. To my colleague and friend, Filipe Verri, with whom significant work has been done, I am also grateful. I like to believe that, while working side by side, we have inaugurated a new form of communication marked by endless brainstorming, treating each other as sounding boards for ideas. The members of the Bio-inspired Computing Laboratory have been an invaluable resource for ideas and assistance, in particular, Daniel Cestari, for both his insights and patience. Though far away, my family is my source of strength, and has always supported and advised me over the years toward a meaningful life. Mom, Dad, Angela, Ricardo, Vlade, Victor, Laura, and Quim, thank you. Finally, I thank the university staff for being so helpful and am grateful for the financial support offered by the Coordination for the Improvement of Higher Education Personnel (CAPES) Brazilian government agency. v ABSTRACT Urio, Paulo Roberto (2017). "Complex network component unfolding using a particle competition technique". Master's dissertation. São Carlos: Instituto de Ciências Matemáticas e de Computação (ICMC/USP), 75 This work applies complex network theory to the problem of semi-supervised and unsupervised learning in networks that are representations of multivariate datasets. Complex networks allow the use of nonlinear dynamical systems to represent behaviors according to the connectivity patterns of networks. Inspired by behavior observed in nature, such as competition for limited resources, dynamical system models can be employed to uncover the organizational structure of a network. In this dissertation, we develop a technique for classifying data represented as interaction networks. As part of the technique, we model a dynamical system inspired by the biological dynamics of resource competition. So far, similar methods have focused on vertices as the resource of competition. We introduce edges as the resource of competition. In doing so, the connectivity pattern of a network might be used not only in the dynamical system simulation but in the learning task as well.