Machine Learning In Social Networks

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Machine Learning in Social Networks

Manasvi Aggarwal,M.N. Murty
Machine Learning in Social Networks Book PDF

Author : Manasvi Aggarwal,M.N. Murty
Publisher : Springer Nature
Page : 121 pages
File Size : 45,7 Mb
Release : 2020-11-25
Category : Technology & Engineering
ISBN : 9789813340220

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Machine Learning in Social Networks by Manasvi Aggarwal,M.N. Murty Pdf

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.

Machine Learning Techniques for Online Social Networks

Tansel Özyer,Reda Alhajj
Machine Learning Techniques for Online Social Networks Book PDF

Author : Tansel Özyer,Reda Alhajj
Publisher : Springer
Page : 236 pages
File Size : 45,7 Mb
Release : 2018-05-30
Category : Social Science
ISBN : 9783319899329

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Machine Learning Techniques for Online Social Networks by Tansel Özyer,Reda Alhajj Pdf

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

Machine Learning in Social Networks

Manasvi Aggarwal,M.N. Murty
Machine Learning in Social Networks Book PDF

Author : Manasvi Aggarwal,M.N. Murty
Publisher : Unknown
Page : 0 pages
File Size : 50,7 Mb
Release : 2021
Category : Electronic
ISBN : 9813340231

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Machine Learning in Social Networks by Manasvi Aggarwal,M.N. Murty Pdf

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. .

Broad Learning Through Fusions

Jiawei Zhang,Philip S. Yu
Broad Learning Through Fusions Book PDF

Author : Jiawei Zhang,Philip S. Yu
Publisher : Springer
Page : 419 pages
File Size : 55,5 Mb
Release : 2019-06-08
Category : Computers
ISBN : 9783030125288

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Broad Learning Through Fusions by Jiawei Zhang,Philip S. Yu Pdf

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

Learning Automata Approach for Social Networks

Alireza Rezvanian,Behnaz Moradabadi,Mina Ghavipour,Mohammad Mehdi Daliri Khomami,Mohammad Reza Meybodi
Learning Automata Approach for Social Networks Book PDF

Author : Alireza Rezvanian,Behnaz Moradabadi,Mina Ghavipour,Mohammad Mehdi Daliri Khomami,Mohammad Reza Meybodi
Publisher : Springer
Page : 329 pages
File Size : 50,9 Mb
Release : 2019-01-22
Category : Technology & Engineering
ISBN : 9783030107673

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Learning Automata Approach for Social Networks by Alireza Rezvanian,Behnaz Moradabadi,Mina Ghavipour,Mohammad Mehdi Daliri Khomami,Mohammad Reza Meybodi Pdf

This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.

Hidden Link Prediction in Stochastic Social Networks

Pandey, Babita,Khamparia, Aditya
Hidden Link Prediction in Stochastic Social Networks Book PDF

Author : Pandey, Babita,Khamparia, Aditya
Publisher : IGI Global
Page : 281 pages
File Size : 41,9 Mb
Release : 2019-05-03
Category : Computers
ISBN : 9781522590972

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Hidden Link Prediction in Stochastic Social Networks by Pandey, Babita,Khamparia, Aditya Pdf

Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.

Social Network Forensics, Cyber Security, and Machine Learning

P. Venkata Krishna,Sasikumar Gurumoorthy,Mohammad S. Obaidat
Social Network Forensics Cyber Security and Machine Learning Book PDF

Author : P. Venkata Krishna,Sasikumar Gurumoorthy,Mohammad S. Obaidat
Publisher : Springer
Page : 116 pages
File Size : 45,9 Mb
Release : 2018-12-29
Category : Technology & Engineering
ISBN : 9789811314568

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Social Network Forensics, Cyber Security, and Machine Learning by P. Venkata Krishna,Sasikumar Gurumoorthy,Mohammad S. Obaidat Pdf

This book discusses the issues and challenges in Online Social Networks (OSNs). It highlights various aspects of OSNs consisting of novel social network strategies and the development of services using different computing models. Moreover, the book investigates how OSNs are impacted by cutting-edge innovations.

Big Data Analytics

Mrutyunjaya Panda,Ajith Abraham,Aboul Ella Hassanien
Big Data Analytics Book PDF

Author : Mrutyunjaya Panda,Ajith Abraham,Aboul Ella Hassanien
Publisher : CRC Press
Page : 255 pages
File Size : 41,9 Mb
Release : 2018-12-12
Category : Business & Economics
ISBN : 9781351622585

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Big Data Analytics by Mrutyunjaya Panda,Ajith Abraham,Aboul Ella Hassanien Pdf

Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

Recent Advances in Hybrid Metaheuristics for Data Clustering

Sourav De,Sandip Dey,Siddhartha Bhattacharyya
Recent Advances in Hybrid Metaheuristics for Data Clustering Book PDF

Author : Sourav De,Sandip Dey,Siddhartha Bhattacharyya
Publisher : John Wiley & Sons
Page : 196 pages
File Size : 55,5 Mb
Release : 2020-08-24
Category : Computers
ISBN : 9781119551591

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Recent Advances in Hybrid Metaheuristics for Data Clustering by Sourav De,Sandip Dey,Siddhartha Bhattacharyya Pdf

An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors—noted experts on the topic—provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.

Social Computing with Artificial Intelligence

Xun Liang
Social Computing with Artificial Intelligence Book PDF

Author : Xun Liang
Publisher : Springer Nature
Page : 289 pages
File Size : 44,5 Mb
Release : 2020-09-16
Category : Computers
ISBN : 9789811577604

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Social Computing with Artificial Intelligence by Xun Liang Pdf

This book provides a comprehensive introduction to the application of artificial intelligence in social computing, from fundamental data processing to advanced social network computing. To broaden readers’ understanding of the topics addressed, it includes extensive data and a large number of charts and references, covering theories, techniques and applications. It particularly focuses on data collection, data mining, artificial intelligence algorithms in social computing, and several key applications of social computing application, and also discusses network propagation mechanisms and dynamic analysis, which provide useful insights into how information is disseminated in online social networks. This book is intended for readers with a basic knowledge of advanced mathematics and computer science.

Data Mining for Social Network Data

Nasrullah Memon,Jennifer Jie Xu,David L. Hicks,Hsinchun Chen
Data Mining for Social Network Data Book PDF

Author : Nasrullah Memon,Jennifer Jie Xu,David L. Hicks,Hsinchun Chen
Publisher : Springer Science & Business Media
Page : 216 pages
File Size : 51,6 Mb
Release : 2010-06-10
Category : Business & Economics
ISBN : 1441962875

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Data Mining for Social Network Data by Nasrullah Memon,Jennifer Jie Xu,David L. Hicks,Hsinchun Chen Pdf

Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.

Centrality and Diversity in Search

M.N. Murty,Anirban Biswas
Centrality and Diversity in Search Book PDF

Author : M.N. Murty,Anirban Biswas
Publisher : Springer
Page : 94 pages
File Size : 53,6 Mb
Release : 2019-08-14
Category : Computers
ISBN : 9783030247133

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Centrality and Diversity in Search by M.N. Murty,Anirban Biswas Pdf

The concepts of centrality and diversity are highly important in search algorithms, and play central roles in applications of artificial intelligence (AI), machine learning (ML), social networks, and pattern recognition. This work examines the significance of centrality and diversity in representation, regression, ranking, clustering, optimization, and classification. The text is designed to be accessible to a broad readership. Requiring only a basic background in undergraduate-level mathematics, the work is suitable for senior undergraduate and graduate students, as well as researchers working in machine learning, data mining, social networks, and pattern recognition.

Deep Learning for Social Media Data Analytics

Tzung-Pei Hong,Leticia Serrano-Estrada,Akrati Saxena,Anupam Biswas
Deep Learning for Social Media Data Analytics Book PDF

Author : Tzung-Pei Hong,Leticia Serrano-Estrada,Akrati Saxena,Anupam Biswas
Publisher : Springer Nature
Page : 297 pages
File Size : 54,5 Mb
Release : 2022-09-18
Category : Computers
ISBN : 9783031108693

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Deep Learning for Social Media Data Analytics by Tzung-Pei Hong,Leticia Serrano-Estrada,Akrati Saxena,Anupam Biswas Pdf

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics.

Graph Machine Learning

Claudio Stamile,Aldo Marzullo,Enrico Deusebio
Graph Machine Learning Book PDF

Author : Claudio Stamile,Aldo Marzullo,Enrico Deusebio
Publisher : Packt Publishing Ltd
Page : 338 pages
File Size : 44,9 Mb
Release : 2021-06-25
Category : Computers
ISBN : 9781800206755

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Graph Machine Learning by Claudio Stamile,Aldo Marzullo,Enrico Deusebio Pdf

Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

Advanced Applications of NLP and Deep Learning in Social Media Data

Abd El-Latif, Ahmed A.,Wani, Mudasir Ahmad,El-Affendi, Mohammed A.
Advanced Applications of NLP and Deep Learning in Social Media Data Book PDF

Author : Abd El-Latif, Ahmed A.,Wani, Mudasir Ahmad,El-Affendi, Mohammed A.
Publisher : IGI Global
Page : 325 pages
File Size : 40,9 Mb
Release : 2023-06-05
Category : Computers
ISBN : 9781668469118

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Advanced Applications of NLP and Deep Learning in Social Media Data by Abd El-Latif, Ahmed A.,Wani, Mudasir Ahmad,El-Affendi, Mohammed A. Pdf

Social media platforms are one of the main generators of textual data where people around the world share their daily life experiences and information with online society. The social, personal, and professional lives of people on these social networking sites generate not only a huge amount of data but also open doors for researchers and academicians with numerous research opportunities. This ample amount of data needs advanced machine learning, deep learning, and intelligent tools and techniques to receive, process, and interpret the information to resolve real-life challenges and improve the online social lives of people. Advanced Applications of NLP and Deep Learning in Social Media Data bridges the gap between natural language processing (NLP), advanced machine learning, deep learning, and online social media. It hopes to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. Covering topics such as machine learning-based prediction, emotion recognition, and high-dimensional text clustering, this premier reference source is an essential resource for OSN service providers, psychiatrists, psychologists, clinicians, sociologists, students and educators of higher education, librarians, researchers, and academicians.