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Mining Graph Data
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Book Synopsis Mining Graph Data by : Diane J. Cook
Download or read book Mining Graph Data written by Diane J. Cook and published by John Wiley & Sons. This book was released on 2006-12-18 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.
Book Synopsis Managing and Mining Graph Data by : Charu C. Aggarwal
Download or read book Managing and Mining Graph Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-02-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.
Book Synopsis Practical Graph Mining with R by : Nagiza F. Samatova
Download or read book Practical Graph Mining with R written by Nagiza F. Samatova and published by CRC Press. This book was released on 2013-07-15 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or cluste
Book Synopsis Graph Mining by : Deepayan Chakrabarti
Download or read book Graph Mining written by Deepayan Chakrabarti and published by Morgan & Claypool Publishers. This book was released on 2012-10-01 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions
Download or read book Graph Data Mining written by Qi Xuan and published by Springer Nature. This book was released on 2021-07-15 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic – the security of graph data mining – and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.
Book Synopsis Exploiting Semantic Web Knowledge Graphs in Data Mining by : P. Ristoski
Download or read book Exploiting Semantic Web Knowledge Graphs in Data Mining written by P. Ristoski and published by IOS Press. This book was released on 2019-06-28 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.
Book Synopsis Mining of Massive Datasets by : Jure Leskovec
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Book Synopsis Graph-Theoretic Techniques for Web Content Mining by : Adam Schenker
Download or read book Graph-Theoretic Techniques for Web Content Mining written by Adam Schenker and published by World Scientific. This book was released on 2005-05-31 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance — a relatively new approach for determining graph similarity — the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms. To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters. In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data using multidimensional scaling. Contents:Introduction to Web MiningGraph Similarity TechniquesGraph Models for Web DocumentsGraph-Based ClusteringGraph-Based ClassificationThe Graph Hierarchy Construction Algorithm for Web Search Clustering Readership: Researchers and graduate students who are interested in computer science, specifically machine learning. Also of interest to researchers in academia or industry in disciplines such as information science or information technology who are interested in text and web documents. Keywords:Graph;Machine Learning;Web Mining;Data Mining;Clustering;Classification;Graph Distance;Maximum Common SubgraphKey Features:Opens up exciting new possibilities for utilizing graphs in common machine learning algorithmsPresents experimental results comparing differing graph representations and graph distance measuresProvides a review of graph-theoretic similarity techniques
Book Synopsis Data Mining and Analysis by : Mohammed J. Zaki
Download or read book Data Mining and Analysis written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2014-05-12 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Book Synopsis Data Mining and Machine Learning by : Mohammed J. Zaki
Download or read book Data Mining and Machine Learning written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2020-01-30 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
Book Synopsis R and Data Mining by : Yanchang Zhao
Download or read book R and Data Mining written by Yanchang Zhao and published by Academic Press. This book was released on 2012-12-31 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work
Book Synopsis Research and Development in Intelligent Systems XXVI by : Richard Ellis
Download or read book Research and Development in Intelligent Systems XXVI written by Richard Ellis and published by Springer Science & Business Media. This book was released on 2009-10-28 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most common document formalisation for text classi?cation is the vector space model founded on the bag of words/phrases representation. The main advantage of the vector space model is that it can readily be employed by classi?cation - gorithms. However, the bag of words/phrases representation is suited to capturing only word/phrase frequency; structural and semantic information is ignored. It has been established that structural information plays an important role in classi?cation accuracy [14]. An alternative to the bag of words/phrases representation is a graph based rep- sentation, which intuitively possesses much more expressive power. However, this representation introduces an additional level of complexity in that the calculation of the similarity between two graphs is signi?cantly more computationally expensive than between two vectors (see for example [16]). Some work (see for example [12]) has been done on hybrid representations to capture both structural elements (- ing the graph model) and signi?cant features using the vector model. However the computational resources required to process this hybrid model are still extensive.
Download or read book Data Mining written by Charu C. Aggarwal and published by Springer. This book was released on 2015-04-13 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Download or read book Graph Algorithms written by Mark Needham and published by "O'Reilly Media, Inc.". This book was released on 2019-05-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark
Book Synopsis Graph Algorithms for Data Science by : Tomaž Bratanic
Download or read book Graph Algorithms for Data Science written by Tomaž Bratanic and published by Simon and Schuster. This book was released on 2024-02-27 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.
Book Synopsis Mining Complex Networks by : Bogumil Kaminski
Download or read book Mining Complex Networks written by Bogumil Kaminski and published by CRC Press. This book was released on 2021-12-15 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platforms are close friends). Link prediction (who is likely to connect to whom on such platforms). Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests). Influential node detection (which social media users would be the best ambassadors of a specific product). This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material. Bogumił Kamiński is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumił is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem. Paweł Prałat is a Professor of Mathematics in Ryerson University, whose main research interests are in random graph theory, especially in modeling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics in The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and three books with 130 plus collaborators. François Théberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 where he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.
Book Synopsis Research and Trends in Data Mining Technologies and Applications by : Taniar, David
Download or read book Research and Trends in Data Mining Technologies and Applications written by Taniar, David and published by IGI Global. This book was released on 2006-10-31 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. Research and Trends in Data Mining Technologies and Applications focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.
