This book presents the features and advantages offered by complex networks in the machine learning domain. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data.

Introduction.- Complex Networks.- Machine Learning.- Network Construction Techniques.- Network-Based Supervised Learning.- Network-Based Unsupervised Learning.- Network-Based Semi-Supervised Learning.- Case Study of Network-Based Supervised Learning: High-Level Data Classification.- Case Study of Network-Based Unsupervised Learning: Stochastic Competitive Learning in Networks.- Case Study of Network-Based Semi-Supervised Learning: Stochastic Competitive-Cooperative Learning in Networks.

This book explores the features and advantages offered by complex networks in the domain of machine learning. In the first part of the book, we present an overview on complex networks and machine learning. Then, we provide a comprehensive description on network-based machine learning. In addition, we also address the important network construction issue. In the second part of the book, we describe some techniques for supervised, unsupervised, and semi-supervised learning that rely on complex networks to perform the learning process. Particularly, we thoroughly investigate a particle competition technique for both unsupervised and semi-supervised learning that is modeled using a stochastic nonlinear dynamical system. Moreover, we supply an analytical analysis of the model, which enables one to predict the behavior of the proposed technique. In addition, we deal with data reliability issues or imperfect data in semi-supervised learning. Even though with relevant practical importance,little research is found about this topic in the literature. In order to validate these techniques, we employ broadly accepted real-world and artificial data sets. Regarding network-based supervised learning, we present a hybrid data classification technique that combines both low and high orders of learning. The low-level term can be implemented by any traditional classification technique, while the high-level term is realized by the extraction of topological features of the underlying network constructed from the input data. Thus, the former classifies test instances according to their physical features, while the latter measures the compliance of test instances with the pattern formation of the data. We show that the high-level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn may generate broad interests to scientific community, mainly to computer science and engineering areas.

“The book explores the combined area of complex network-based machine learning. It presents the theoretical concepts underlying the two complementary parts as well as those related to their interaction with respect to supervised, unsupervised and semi-supervised learning. The latest developments in the field, together with real-world test scenarios, are additionally treated in detail.” (Catalin Stoean, zbMATH 1357.68003, 2017)

This book combines two important and popular research areas: complex networks and machine learning This book contains not only fundamental background, but also recent research results Numerous illustrative figures and step-by-step examples help readers to understand the main idea and implementation details Includes supplementary material: sn.pub/extras