Machine Learning in Complex Networks

His current research interests include machine learning, complex networks, artificial neural networks, and pattern recognition. He has published more than 180 scientific articles in refereed international journals, books, ...

Machine Learning in Complex Networks

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. 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. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the 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 will generate broad interests to scientific community, mainly to computer science and engineering areas.

More Books:

Machine Learning in Complex Networks
Language: en
Pages: 331
Authors: Thiago Christiano Silva, Liang Zhao
Categories: Computers
Type: BOOK - Published: 2016-01-28 - Publisher: Springer

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks
Computation in Complex Networks
Language: en
Pages: 352
Authors: Clara Pizzuti, Annalisa Socievole
Categories: Technology & Engineering
Type: BOOK - Published: 2021-09-02 - Publisher: MDPI

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical,
Dynamics On and Of Complex Networks III
Language: en
Pages: 244
Authors: Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi, Jean-Charles Delvenne, Bivas Mitra
Categories: Science
Type: BOOK - Published: 2019-05-13 - Publisher: Springer

This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring
Graph-Powered Machine Learning
Language: en
Pages: 496
Authors: Alessandro Nego
Categories: Computers
Type: BOOK - Published: 2021-10-05 - Publisher: Simon and Schuster

At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you
Towards Mathematics, Computers and Environment: A Disasters Perspective
Language: en
Pages: 258
Authors: Leonardo Bacelar Lima Santos, Rogério Galante Negri, Tiago José de Carvalho
Categories: Mathematics
Type: BOOK - Published: 2019-07-23 - Publisher: Springer

With relevant, timely topics, this book gathers carefully selected, peer-reviewed scientific works and offers a glimpse of the state-of-the-art in disaster prevention research, with an emphasis on challenges in Latin America. Topics include studies on surface frost, an extreme meteorological event that occasionally affects parts of Argentina, Bolivia, Peru, and