Link Prediction In Social Networks
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Link Prediction In Social Networks
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Author : Srinivas Virinchi
language : en
Publisher: Springer
Release Date : 2016-01-22
Link Prediction In Social Networks written by Srinivas Virinchi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-22 with Computers categories.
This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.
Hidden Link Prediction In Stochastic Social Networks
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Author : Pandey, Babita
language : en
Publisher: IGI Global
Release Date : 2019-05-03
Hidden Link Prediction In Stochastic Social Networks written by Pandey, Babita and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-03 with Computers categories.
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.
Link Prediction In Social Networks By Neutrosophic Graph
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Author : Rupkumar Mahapatra
language : en
Publisher: Infinite Study
Release Date :
Link Prediction In Social Networks By Neutrosophic Graph written by Rupkumar Mahapatra and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
The computation of link prediction is one of the most important tasks on a social network. Several methods are available in the literature to predict links in networks and RSM index is one of them. The RSM index is applicable in the fuzzy environment and it does not incorporate the notion of falsity and indecency parameters which occur frequently in uncertain environments. In the present method, the behaviors of the common neighbor and the other parameters, like nature of job, location, etc., are considered. In this paper, more parameters are included in the RSM index for making it more flexible and realistic and it is best fitted in the neutrosophic environment. Many important properties are studied for this modified RSM index. A small network from Facebook is considered to illustrate the problem.
Social Network Data Analytics
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Author : Charu C. Aggarwal
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-18
Social Network Data Analytics written by Charu C. Aggarwal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-18 with Computers categories.
Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.
Prediction And Inference From Social Networks And Social Media
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Author : Jalal Kawash
language : en
Publisher: Springer
Release Date : 2017-03-16
Prediction And Inference From Social Networks And Social Media written by Jalal Kawash and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-16 with Computers categories.
This book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives; they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field.
Graph Neural Networks Foundations Frontiers And Applications
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Author : Lingfei Wu
language : en
Publisher: Springer Nature
Release Date : 2022-01-03
Graph Neural Networks Foundations Frontiers And Applications written by Lingfei Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-03 with Computers categories.
Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Graph Theoretic Approaches For Analyzing Large Scale Social Networks
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Author : Natarajan Meghanathan
language : en
Publisher: Information Science Reference
Release Date : 2018
Graph Theoretic Approaches For Analyzing Large Scale Social Networks written by Natarajan Meghanathan and has been published by Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computers categories.
"This book brings together the recent research advances in the field of graph theory for analyzing large-scale social networks. It brings together the research advances in state-of-the-art graph theory algorithms and techniques that have contributed to the effective analysis of social networks, especially those networks that generate significant amount of data and involve several thousands of users"--
Cellular Learning Automata Theory And Applications
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Author : Reza Vafashoar
language : en
Publisher: Springer Nature
Release Date : 2020-07-24
Cellular Learning Automata Theory And Applications written by Reza Vafashoar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-24 with Technology & Engineering categories.
This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA’s parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments. The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.
Trends In Social Network Analysis
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Author : Rokia Missaoui
language : en
Publisher: Springer
Release Date : 2017-04-29
Trends In Social Network Analysis written by Rokia Missaoui and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-29 with Computers categories.
The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field.
Social Sensing
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Author : Dong Wang
language : en
Publisher: Morgan Kaufmann
Release Date : 2015-04-17
Social Sensing written by Dong Wang and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-17 with Computers categories.
Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book