Machine Learning in Complex NetworksMachine Learning in Complex 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.

Author: Thiago Christiano Silva

Publisher: Springer

ISBN: 9783319172903

Category:

Page: 331

View: 583

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.

Dynamics On and Of Complex Networks IIIDynamics On and Of Complex Networks III



This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics.

Author: Fakhteh Ghanbarnejad

Publisher: Springer

ISBN: 9783030146832

Category:

Page: 244

View: 660

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 network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes. The book contains extended versions of high-quality submissions received at the workshop, Dynamics On and Of Complex Networks (doocn.org), together with new invited contributions. The chapters will benefit a diverse community of researchers. The book is suitable for graduate students, postdoctoral researchers and professors of various disciplines including sociology, physics, mathematics, and computer science.

Complex Networks and Their Applications VIIComplex Networks and Their Applications VII



Bringing a Feature Selection Metric from Machine Learning to Complex Networks Nicolas Dugué1(B), Jean-Charles Lamirel2, and Anthony Perez3 1 Le Mans Université, LIUM EA 4023, Laboratoire d'Informatique de l'Université du Mans, ...

Author: Luca Maria Aiello

Publisher: Springer

ISBN: 9783030054144

Category:

Page: 677

View: 238

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Optimization Learning and Control for Interdependent Complex NetworksOptimization Learning and Control for Interdependent Complex Networks



Chapter 6 An Introduction to Advanced Machine Learning: Meta-Learning Algorithms, Applications, and Promises Farid Ghareh ... Optimization, Learning, and Control for Interdependent Complex Networks, Advances in Intelligent Systems and ...

Author: M. Hadi Amini

Publisher: Springer Nature

ISBN: 9783030340940

Category:

Page: 304

View: 830

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.

Statistical and Machine Learning Approaches for Network AnalysisStatistical and Machine Learning Approaches for Network Analysis



The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Author: Matthias Dehmer

Publisher: John Wiley & Sons

ISBN: 9781118346983

Category:

Page: 344

View: 176

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Complex Networks Their Applications IXComplex Networks Their Applications IX



To estimate the performance of these models in a supervised machine learning model, disjoint and independent train and test sets are needed. However, objects in a real-world network are inherently related to each other.

Author: Rosa M. Benito

Publisher: Springer Nature

ISBN: 9783030653514

Category:

Page: 715

View: 219

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the IX International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020). The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, network dynamics; diffusion, epidemics and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks and technological networks.

Machine Learning in Social NetworksMachine Learning in Social Networks



This book deals with network representation learning.

Author: Manasvi Aggarwal

Publisher: Springer Nature

ISBN: 9789813340220

Category:

Page: 112

View: 121

This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties.

Complex Networks and Their Applications VIIIComplex Networks and Their Applications VIII



Over the years, so many network embedding models have been worked upon based on several approaches. In this paper, we study vector embeddings of 10 different representation learning models, with the sole aim of carrying out two machine ...

Author: Hocine Cherifi

Publisher: Springer Nature

ISBN: 9783030366872

Category:

Page: 979

View: 142

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Synchronization Swarming and Emergent Behaviors in Complex Networks and NeuroscienceSynchronization Swarming and Emergent Behaviors in Complex Networks and Neuroscience



Machine learning dynamical phase transitions in complex networks. PhysRev E. (2019) 100:052312. doi: 10.1103/PhysRevE.100.052312 54. Fan H, Jiang J, Zhang C, Wang X, Lai YC. Long-term prediction of chaotic systems with machine learning.

Author: Andrea Duggento

Publisher: Frontiers Media SA

ISBN: 9782889745036

Category:

Page:

View: 805