Finding Communities In Social Networks Using Graph Embeddings
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Finding Communities In Social Networks Using Graph Embeddings
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Author : Mosab Alfaqeeh
language : en
Publisher: Springer Nature
Release Date : 2024-06-29
Finding Communities In Social Networks Using Graph Embeddings written by Mosab Alfaqeeh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-29 with Computers categories.
Community detection in social networks is an important but challenging problem. This book develops a new technique for finding communities that uses both structural similarity and attribute similarity simultaneously, weighting them in a principled way. The results outperform existing techniques across a wide range of measures, and so advance the state of the art in community detection. Many existing community detection techniques base similarity on either the structural connections among social-network users, or on the overlap among the attributes of each user. Either way loses useful information. There have been some attempts to use both structure and attribute similarity but success has been limited. We first build a large real-world dataset by crawling Instagram, producing a large set of user profiles. We then compute the similarity between pairs of users based on four qualitatively different profile properties: similarity of language used in posts, similarity of hashtags used (which requires extraction of content from them), similarity of images displayed (which requires extraction of what each image is 'about'), and the explicit connections when one user follows another. These single modality similarities are converted into graphs. These graphs have a common node set (the users) but different sets a weighted edges. These graphs are then connected into a single larger graph by connecting the multiple nodes representing the same user by a clique, with edge weights derived from a lazy random walk view of the single graphs. This larger graph can then be embedded in a geometry using spectral techniques. In the embedding, distance corresponds to dissimilarity so geometric clustering techniques can be used to find communities. The resulting communities are evaluated using the entire range of current techniques, outperforming all of them. Topic modelling is also applied to clusters to show that they genuinely represent users with similar interests. This can form the basis for applications such as online marketing, or key influence selection.
Knowledge Science Engineering And Management
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Author : Christos Douligeris
language : en
Publisher: Springer Nature
Release Date : 2019-08-20
Knowledge Science Engineering And Management written by Christos Douligeris and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-20 with Computers categories.
This two-volume set of LNAI 11775 and LNAI 11776 constitutes the refereed proceedings of the 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019, held in Athens, Greece, in August 2019. The 77 revised full papers and 23 short papers presented together with 10 poster papers were carefully reviewed and selected from 240 submissions. The papers of the first volume are organized in the following topical sections: Formal Reasoning and Ontologies; Recommendation Algorithms and Systems; Social Knowledge Analysis and Management ; Data Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge Engineering Applications. The papers of the second volume are organized in the following topical sections: Probabilistic Models and Applications; Text Mining and Document Analysis; Knowledge Theories and Models; and Network Knowledge Representation and Learning.
Graph Databases In Action
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Author : Josh Perryman
language : en
Publisher: Simon and Schuster
Release Date : 2020-10-17
Graph Databases In Action written by Josh Perryman and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-17 with Computers categories.
Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization. Summary Relationships in data often look far more like a web than an orderly set of rows and columns. Graph databases shine when it comes to revealing valuable insights within complex, interconnected data such as demographics, financial records, or computer networks. In Graph Databases in Action, experts Dave Bechberger and Josh Perryman illuminate the design and implementation of graph databases in real-world applications. You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Isolated data is a thing of the past! Now, data is connected, and graph databases—like Amazon Neptune, Microsoft Cosmos DB, and Neo4j—are the essential tools of this new reality. Graph databases represent relationships naturally, speeding the discovery of insights and driving business value. About the book Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization. What's inside Graph databases vs. relational databases Systematic graph data modeling Querying and navigating a graph Graph patterns Pitfalls and antipatterns About the reader For software developers. No experience with graph databases required. About the author Dave Bechberger and Josh Perryman have decades of experience building complex data-driven systems and have worked with graph databases since 2014. Table of Contents PART 1 - GETTING STARTED WITH GRAPH DATABASES 1 Introduction to graphs 2 Graph data modeling 3 Running basic and recursive traversals 4 Pathfinding traversals and mutating graphs 5 Formatting results 6 Developing an application PART 2 - BUILDING ON GRAPH DATABASES 7 Advanced data modeling techniques 8 Building traversals using known walks 9 Working with subgraphs PART 3 - MOVING BEYOND THE BASICS 10 Performance, pitfalls, and anti-patterns 11 What's next: Graph analytics, machine learning, and resources
Machine Learning In Social Networks
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Author : Manasvi Aggarwal
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Machine Learning In Social Networks written by Manasvi Aggarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Technology & Engineering categories.
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.
Enhancing Security In Public Spaces Through Generative Adversarial Networks Gans
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Author : Ponnusamy, Sivaram
language : en
Publisher: IGI Global
Release Date : 2024-05-16
Enhancing Security In Public Spaces Through Generative Adversarial Networks Gans written by Ponnusamy, Sivaram and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Computers categories.
As the demand for data security intensifies, the vulnerabilities become glaring, exposing sensitive information to potential threats. In this tumultuous landscape, Generative Adversarial Networks (GANs) emerge as a groundbreaking solution, transcending their initial role as image generators to become indispensable guardians of data security. Within the pages of Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs), readers are guided through the intricate world of GANs, unraveling their unique design and dynamic adversarial training. The book presents GANs not merely as a technical marvel but as a strategic asset for organizations, offering a comprehensive solution to fortify cybersecurity, protect data privacy, and mitigate the risks associated with evolving cyber threats. It navigates the ethical considerations surrounding GANs, emphasizing the delicate balance between technological advancement and responsible use.
Pricai 2023 Trends In Artificial Intelligence
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Author : Fenrong Liu
language : en
Publisher: Springer Nature
Release Date : 2023-11-10
Pricai 2023 Trends In Artificial Intelligence written by Fenrong Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-10 with Computers categories.
This three-volume set, LNCS 14325-14327 constitutes the thoroughly refereed proceedings of the 20th Pacific Rim Conference on Artificial Intelligence, PRICAI 2023, held in Jakarta, Indonesia, in November 2023. The 95 full papers and 36 short papers presented in these volumes were carefully reviewed and selected from 422 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc.
Practical Social Network Analysis With Python
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Author : Krishna Raj P.M.
language : en
Publisher: Springer
Release Date : 2018-08-25
Practical Social Network Analysis With Python written by Krishna Raj P.M. and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-25 with Computers categories.
This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain.
Handbook Of Matrices
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Author : Helmut L?tkepohl
language : en
Publisher:
Release Date : 1996-11-05
Handbook Of Matrices written by Helmut L?tkepohl and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-11-05 with Mathematics categories.
Matrices are used in many areas including statistics, natural sciences, econometrics, maths & engineering. This book provides a collection of results for easy reference in one source, along with a comprehensive dictionary of matrices & related terms.
Social Networks Analysis And Mining
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Author : Luca Maria Aiello
language : en
Publisher: Springer Nature
Release Date : 2025-01-23
Social Networks Analysis And Mining written by Luca Maria Aiello and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-23 with Computers categories.
This LNCS conference 4-volume set constitutes the proceedings of the 16th International Conference on Social Networks Analysis and Mining, ASONAM 2024, in Rende, Italy, during September 2–5, 2024. The 33 full papers together with 36 short papers included in this volume were carefully reviewed and selected from 167 submissions. The conference covers a wide spectrum of research contributions to the foundations and applications of social networks.
From Social Data Mining And Analysis To Prediction And Community Detection
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Author : Mehmet Kaya
language : en
Publisher: Springer
Release Date : 2017-03-21
From Social Data Mining And Analysis To Prediction And Community Detection written by Mehmet Kaya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-21 with Computers categories.
This book presents the state-of-the-art in various aspects of analysis and mining of online social networks. Within the broader context of online social networks, it focuses on important and upcoming topics of social network analysis and mining such as the latest in sentiment trends research and a variety of techniques for community detection and analysis. The book collects chapters that are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2015), which was held in Paris, France in August 2015. All papers have been peer reviewed and checked carefully for overlap with the literature. The book will appeal to students and researchers in social network analysis/mining and machine learning.