Download or read book Complex Networks Their Applications XII written by Hocine Cherifi and published by Springer Nature. This book was released on 2024 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Zusammenfassung: 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 XII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2023). The carefully selected papers cover a wide range of theoretical topics such as network embedding and network geometry; community structure, network dynamics; diffusion, epidemics and spreading processes; machine learning and graph neural networks as well as all the main network applications, including social and political networks; networks in finance and economics; biological networks and technological networks
Download or read book Advanced Methods for Complex Network Analysis written by Meghanathan, Natarajan and published by IGI Global. This book was released on 2016-04-07 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: As network science and technology continues to gain popularity, it becomes imperative to develop procedures to examine emergent network domains, as well as classical networks, to help ensure their overall optimization. Advanced Methods for Complex Network Analysis features the latest research on the algorithms and analysis measures being employed in the field of network science. Highlighting the application of graph models, advanced computation, and analytical procedures, this publication is a pivotal resource for students, faculty, industry practitioners, and business professionals interested in theoretical concepts and current developments in network domains.
Download or read book Online Social Networks in Business Frameworks written by Sudhir Kumar Rathi and published by John Wiley & Sons. This book was released on 2024-09-17 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a vital method for companies to connect with potential clients andconsumers in the digital era of Online Social Networks (OSNs), utilizing the strengthof well-known social networks and AI to achieve success through fostering brandsupporters, generating leads, and enhancing customer interactions. There are currently 4.8 billion Online Social Network (OSN) users worldwide. Online Social Networks in Business Frameworks presents marketing through online social networks (OSNs), which is a potent method for companies of all sizes to connect with potential clients and consumers. If visitors are not on OSN sites like Facebook, Twitter, and LinkedIn, they are missing out on the fact that people discover, learn about, follow, and purchase from companies on OSNs. Excellent OSN advertising may help a company achieve amazing success by fostering committed brand supporters and even generating leads and revenue. A type of digital advertising known as social media marketing (SMM) makes use of the strength of well-known social networks to further advertise and establish branding objectives. Nevertheless, it goes beyond simply setting up company accounts and tweeting whenever visitors feel like it. Preserving and improving profiles means posting content that represents the company and draws in the right audience, such as images, videos, articles, and live videos, addressing comments, shares, and likes while keeping an eye on the reputation to create a brand network, and following and interacting with followers, clients, and influencers.
Download or read book Big Data of Complex Networks written by Matthias Dehmer and published by CRC Press. This book was released on 2016-08-19 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: Provides a complete discussion of both the hardware and software used to organize big data Describes a wide range of useful applications for managing big data and resultant data sets Maintains a firm focus on massive data and large networks Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.
Download or read book Machine Learning in Complex Networks written by Thiago Christiano Silva and published by Springer. This book was released on 2016-01-28 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Algorithms and Models for Network Data and Link Analysis written by François Fouss and published by Cambridge University Press. This book was released on 2016-07-12 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. MATLAB®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.
Download or read book Artificial Intelligence Application in Networks and Systems written by Radek Silhavy and published by Springer Nature. This book was released on 2023-07-08 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of artificial intelligence in networks and systems is a rapidly evolving field that has the potential to transform a wide range of industries. The refereed proceedings in this book is from the Artificial Intelligence Application in Networks and Systems session of the Computer Science Online Conference 2023 (CSOC 2023), which was held online in April 2023. The section brings together experts from different fields to present their research and discuss the latest trends and challenges. One of the key themes in this section is the development of intelligent systems that can learn, adapt, and optimize their performance in real time. Researchers are exploring how AI algorithms can be used to create autonomous networks and systems that can make decisions without human intervention. Furthermore, this section highlights the use of AI in improving network performance and efficiency. Researchers are exploring how AI algorithms can be used to optimize network routing, reduce congestion, and improve the quality of service. These efforts can help organizations save costs and improve user experience.
Download or read book Advancing Intelligent Networks Through Distributed Optimization written by Rajest, S. Suman and published by IGI Global. This book was released on 2024-08-29 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smart homes, smart grids, and the IoT, among many other intelligent applications. Intelligent networks are founded on integrating caching and multi-agent systems that optimize data storage and the entire devices learning process. However, a central node through which all agents transmit status messages and reward information is a major drawback of this design pattern. This central node condition instigates more communication overhead, potential data leakage, and the birth of data islands. To reverse this trend, using distributed optimization techniques and methodologies in cache-enabled multi-agent learning environments is increasingly beneficial. Advancing Intelligent Networks Through Distributed Optimization explains the current race for sophisticated and accurate distributed optimization in cache-enabled intelligent IoT networks given the need to make multi-agent learning converge faster and reduce communication overhead. These techniques will require innovative resource allocation strategies stretching from system training to caching, communication, and processing amongst millions of agents. This book combines the key recent research in these races into a single binder that can serve all the interested theoretical and practical scholars. The book focuses broadly on intelligent systems optimization trends. It identifies the various applications of advanced distributed optimization from manufacturing to medicine, agriculture and smart cities.
Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Download or read book 6GN for Future Wireless Networks written by Xiaofei Wang and published by Springer Nature. This book was released on 2021-01-28 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Third International Conference on 6G for Future Wireless Networks, 6GN 2020, held in Tianjin, China, in August 2020. The conference was held virtually due to the COVID-19 pandemic. The 45 full papers were selected from 109 submissions and present the state of the art and practical applications of 6G technologies. The papers are arranged thematically on network scheduling and optimization; wireless system and platform; intelligent applications; network performance evaluation; cyber security and privacy; technologies for private 5G/6G.
Download or read book ADVANCED MACHINE LEARNING ALGORITHMS written by Mr. Rajesh Sen and published by Xoffencerpublication. This book was released on 2024-04-18 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of artificial intelligence has reached a greater degree of complexity with the introduction of advanced machine learning algorithms. When compared to more conventional approaches, these algorithms are more exhaustive in their examination of data analysis, pattern detection, and decision-making procedures. This is an overview that serves as an introduction. Deep learning is a subfield of machine learning in which artificial neural networks, which are modelled after the structure and function of the human brain, are taught to discover new information by analyzing huge volumes of data. For example, Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for sequential data analysis are examples of deep learning models that have achieved great success in a variety of disciplines, including computer vision, natural language processing, and speech recognition. Through the process of reinforcement learning, agents are taught to make sequences of decisions within an environment in order to maximize the accumulation of overall rewards. Reinforcement learning agents learn by trial and error, getting feedback in the form of incentives or penalties. This is in contrast to supervised learning, which offers the model data that has been labelled. The use of this strategy has shown to be effective in a variety of domains, including robotics, autonomous vehicle control, and game playing (for example, AlphaGo). Deep learning models that fall into the GAN category were first presented by Ian Good fellow in the year 2014. Generalized adversarial networks (GANs) are made up of two neural networks—a generator and a discriminator—that are trained concurrently in a competitive environment. It is the discriminator's job to learn how to distinguish between genuine and false data, while the generator is responsible for learning how to make synthetic data samples that are similar to actual data. Application areas for GANs include the production of images, the enhancement of data, and the transfer of styles. This particular sort of deep learning model, known as transformers, has been increasingly popular in the field of natural language processing (NLP) initiatives. Transformers, in contrast to more conventional sequence models such as recurrent neural networks (RNNs) and long short-term
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Download or read book Complex Networks and Their Applications XI written by Hocine Cherifi and published by Springer Nature. This book was released on 2023-01-25 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 XI International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2022). 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.
Download or read book Network Connectivity written by Chen Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks naturally appear in many high-impact domains, ranging from social network analysis to disease dissemination studies to infrastructure system design. Within network studies, network connectivity plays an important role in a myriad of applications. The diversity of application areas has spurred numerous connectivity measures, each designed for some specific tasks. Depending on the complexity of connectivity measures, the computational cost of calculating the connectivity score can vary significantly. Moreover, the complexity of the connectivity would predominantly affect the hardness of connectivity optimization, which is a fundamental problem for network connectivity studies. This book presents a thorough study in network connectivity, including its concepts, computation, and optimization. Specifically, a unified connectivity measure model will be introduced to unveil the commonality among existing connectivity measures. For the connectivity computation aspect, the authors introduce the connectivity tracking problems and present several effective connectivity inference frameworks under different network settings. Taking the connectivity optimization perspective, the book analyzes the problem theoretically and introduces an approximation framework to effectively optimize the network connectivity. Lastly, the book discusses the new research frontiers and directions to explore for network connectivity studies. This book is an accessible introduction to the study of connectivity in complex networks. It is essential reading for advanced undergraduates, Ph.D. students, as well as researchers and practitioners who are interested in graph mining, data mining, and machine learning.
Download or read book Complex Networks and Their Applications VII written by Luca Maria Aiello and published by Springer. This book was released on 2018-12-05 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book 7th EAI International Conference on Robotic Sensor Networks written by Ömer Melih Gül and published by Springer Nature. This book was released on with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Optimization in Complex Networks written by My T. Thai and published by Springer Science & Business Media. This book was released on 2011-11-25 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex Social Networks is a newly emerging (hot) topic with applications in a variety of domains, such as communication networks, engineering networks, social networks, and biological networks. In the last decade, there has been an explosive growth of research on complex real-world networks, a theme that is becoming pervasive in many disciplines, ranging from mathematics and computer science to the social and biological sciences. Optimization of complex communication networks requires a deep understanding of the interplay between the dynamics of the physical network and the information dynamics within the network. Although there are a few books addressing social networks or complex networks, none of them has specially focused on the optimization perspective of studying these networks. This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology.
