Managing And Mining Graph Data

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Managing and Mining Graph Data

Charu C. Aggarwal,Haixun Wang
Managing and Mining Graph Data Book PDF

Author : Charu C. Aggarwal,Haixun Wang
Publisher : Springer Science & Business Media
Page : 623 pages
File Size : 48,6 Mb
Release : 2010-02-02
Category : Computers
ISBN : 9781441960450

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Managing and Mining Graph Data by Charu C. Aggarwal,Haixun Wang Pdf

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.

Graph Data Mining

Qi Xuan,Zhongyuan Ruan,Yong Min
Graph Data Mining Book PDF

Author : Qi Xuan,Zhongyuan Ruan,Yong Min
Publisher : Springer Nature
Page : 256 pages
File Size : 45,8 Mb
Release : 2021-07-15
Category : Computers
ISBN : 9789811626098

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Graph Data Mining by Qi Xuan,Zhongyuan Ruan,Yong Min Pdf

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.

Graph Mining

Deepayan Chakrabarti,Christos Faloutsos
Graph Mining Book PDF

Author : Deepayan Chakrabarti,Christos Faloutsos
Publisher : Springer Nature
Page : 191 pages
File Size : 43,9 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031019036

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Graph Mining by Deepayan Chakrabarti,Christos Faloutsos Pdf

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

Mining Graph Data

Diane J. Cook,Lawrence B. Holder
Mining Graph Data Book PDF

Author : Diane J. Cook,Lawrence B. Holder
Publisher : John Wiley & Sons
Page : 501 pages
File Size : 47,8 Mb
Release : 2006-12-18
Category : Technology & Engineering
ISBN : 9780470073032

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Mining Graph Data by Diane J. Cook,Lawrence B. Holder Pdf

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.

Graph Data Management

George Fletcher,Jan Hidders,Josep Lluís Larriba-Pey
Graph Data Management Book PDF

Author : George Fletcher,Jan Hidders,Josep Lluís Larriba-Pey
Publisher : Springer
Page : 186 pages
File Size : 47,7 Mb
Release : 2018-10-31
Category : Computers
ISBN : 9783319961934

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Graph Data Management by George Fletcher,Jan Hidders,Josep Lluís Larriba-Pey Pdf

This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.

Practical Graph Mining with R

Nagiza F. Samatova,William Hendrix,John Jenkins,Kanchana Padmanabhan,Arpan Chakraborty
Practical Graph Mining with R Book PDF

Author : Nagiza F. Samatova,William Hendrix,John Jenkins,Kanchana Padmanabhan,Arpan Chakraborty
Publisher : CRC Press
Page : 495 pages
File Size : 51,7 Mb
Release : 2013-07-15
Category : Business & Economics
ISBN : 9781439860854

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Practical Graph Mining with R by Nagiza F. Samatova,William Hendrix,John Jenkins,Kanchana Padmanabhan,Arpan Chakraborty Pdf

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

Mining of Massive Datasets

Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman
Mining of Massive Datasets Book PDF

Author : Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman
Publisher : Cambridge University Press
Page : 480 pages
File Size : 41,7 Mb
Release : 2014-11-13
Category : Computers
ISBN : 9781107077232

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Mining of Massive Datasets by Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman Pdf

Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Managing and Mining Sensor Data

Charu C. Aggarwal
Managing and Mining Sensor Data Book PDF

Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
Page : 547 pages
File Size : 44,7 Mb
Release : 2013-01-15
Category : Computers
ISBN : 9781461463092

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Managing and Mining Sensor Data by Charu C. Aggarwal Pdf

Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.

Data Mining Techniques

Michael J. A. Berry,Gordon S. Linoff
Data Mining Techniques Book PDF

Author : Michael J. A. Berry,Gordon S. Linoff
Publisher : John Wiley & Sons
Page : 671 pages
File Size : 53,6 Mb
Release : 2004-04-09
Category : Business & Economics
ISBN : 9780471470649

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Data Mining Techniques by Michael J. A. Berry,Gordon S. Linoff Pdf

Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Graph Data Management

Sherif Sakr,Eric Pardede
Graph Data Management Book PDF

Author : Sherif Sakr,Eric Pardede
Publisher : IGI Global
Page : 0 pages
File Size : 46,9 Mb
Release : 2012
Category : Computers
ISBN : 161350053X

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Graph Data Management by Sherif Sakr,Eric Pardede Pdf

"This book is a central reference source for different data management techniques for graph data structures and their applications, discussing graphs for modeling complex structured and schemaless data from the Semantic Web, social networks, protein networks, chemical compounds, and multimedia databases"--Provided by publisher.

Research and Development in Intelligent Systems XXVI

Richard Ellis,Miltos Petridis
Research and Development in Intelligent Systems XXVI Book PDF

Author : Richard Ellis,Miltos Petridis
Publisher : Springer Science & Business Media
Page : 504 pages
File Size : 48,9 Mb
Release : 2009-10-28
Category : Computers
ISBN : 9781848829831

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Research and Development in Intelligent Systems XXVI by Richard Ellis,Miltos Petridis Pdf

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.

Encyclopedia of Data Warehousing and Mining

Wang, John
Encyclopedia of Data Warehousing and Mining Book PDF

Author : Wang, John
Publisher : IGI Global
Page : 1382 pages
File Size : 44,6 Mb
Release : 2005-06-30
Category : Computers
ISBN : 9781591405597

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Encyclopedia of Data Warehousing and Mining by Wang, John Pdf

Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.

Descriptive Data Mining

David L. Olson,Georg Lauhoff
Descriptive Data Mining Book PDF

Author : David L. Olson,Georg Lauhoff
Publisher : Springer
Page : 130 pages
File Size : 43,8 Mb
Release : 2019-05-06
Category : Business & Economics
ISBN : 9789811371813

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Descriptive Data Mining by David L. Olson,Georg Lauhoff Pdf

This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

Data Mining: Concepts and Techniques

Jiawei Han,Micheline Kamber,Jian Pei
Data Mining Concepts and Techniques Book PDF

Author : Jiawei Han,Micheline Kamber,Jian Pei
Publisher : Elsevier
Page : 740 pages
File Size : 55,6 Mb
Release : 2011-06-09
Category : Computers
ISBN : 9780123814807

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Data Mining: Concepts and Techniques by Jiawei Han,Micheline Kamber,Jian Pei Pdf

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Practical Graph Mining with R

William Hendrix
Practical Graph Mining with R Book PDF

Author : William Hendrix
Publisher : Unknown
Page : 0 pages
File Size : 46,8 Mb
Release : 2013
Category : Electronic
ISBN : OCLC:1137344096

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Practical Graph Mining with R by William Hendrix Pdf

Discover Novel and Insightful Knowledge from Data Represented as a Graph Practical 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 clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. Hands-On Application of Graph Data Mining Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks. Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical Foundations Every algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique. Makes Graph Mining Accessible to Various Levels of Expertise Assuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.