Mining Graph Data

Mining Graph Data PDF
Author: Diane J. Cook
Publisher: John Wiley & Sons
ISBN: 0470073039
Size: 62.55 MB
Format: PDF, Kindle
Category : Technology & Engineering
Languages : en
Pages : 434
View: 2853

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Mining Graph Data

by Diane J. Cook, Mining Graph Data Books available in PDF, EPUB, Mobi Format. Download Mining Graph Data books, 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.


Managing And Mining Graph Data

Managing and Mining Graph Data PDF
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 1441960457
Size: 43.84 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 600
View: 457

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Managing And Mining Graph Data

by Charu C. Aggarwal, Managing And Mining Graph Data Books available in PDF, EPUB, Mobi Format. Download Managing And Mining Graph Data books, 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.


Practical Graph Mining With R

Practical Graph Mining with R PDF
Author: Nagiza F. Samatova
Publisher: CRC Press
ISBN: 1439860858
Size: 46.52 MB
Format: PDF, ePub
Category : Business & Economics
Languages : en
Pages : 495
View: 485

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Practical Graph Mining With R

by Nagiza F. Samatova, Practical Graph Mining With R Books available in PDF, EPUB, Mobi Format. Download Practical Graph Mining With R books, 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.


Graph Mining

Graph Mining PDF
Author: Deepayan Chakrabarti
Publisher: Morgan & Claypool Publishers
ISBN: 1608451151
Size: 47.99 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 191
View: 954

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Graph Mining

by Deepayan Chakrabarti, Graph Mining Books available in PDF, EPUB, Mobi Format. Download Graph Mining books, 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


Data Mining

Data Mining PDF
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319141422
Size: 70.18 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 734
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Data Mining

by Charu C. Aggarwal, Data Mining Books available in PDF, EPUB, Mobi Format. Download Data Mining books, 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


Individual And Collective Graph Mining

Individual and Collective Graph Mining PDF
Author: Danai Koutra
Publisher: Morgan & Claypool Publishers
ISBN: 1681730405
Size: 67.98 MB
Format: PDF
Category : Computers
Languages : en
Pages : 206
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Individual And Collective Graph Mining

by Danai Koutra, Individual And Collective Graph Mining Books available in PDF, EPUB, Mobi Format. Download Individual And Collective Graph Mining books, Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas: •Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities. •Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity. The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science, to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.


Hyper Sequence Graph Mining

Hyper sequence graph Mining PDF
Author: Xinran Yu
Publisher:
ISBN: 9781339035116
Size: 65.26 MB
Format: PDF
Category : Algorithms
Languages : en
Pages : 114
View: 1408

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Hyper Sequence Graph Mining

by Xinran Yu, Hyper Sequence Graph Mining Books available in PDF, EPUB, Mobi Format. Download Hyper Sequence Graph Mining books, The era of Big Data has already begun with the promises of several potential benefits in various domains including education, healthcare, economy and so on. Understanding and fully utilizing these potential benefits requires to address many technical issues in various stages from data acquisition to interpretation. One of the key stages is how to extract useful information from real life datasets. Given that this is a very challenging problem even with small size datasets, the research community has been extensively investigating various data mining and searching algorithms in the context of various types of datasets. In this dissertation, a structure called hyper-sequence-graph is investigated. A hyper-sequence-graph represents both sequential relationships and non-sequential relationships in a dataset on one structure. Based on these two types of relationships, the focus will be on mining datasets and exploring useful information. From the sequential attribute site, we consider the well-known frequent pattern mining (FPM) problem and introduce a new form of pattern mining called super-sequence frequent pattern mining (SS-FPM). To solve SS-FPM problem, a directed weighted graph called sequence graph is generated form the given sequential dataset, and then a heuristic algorithm using adjacency matrix iteration technique is proposed. Based on SS-FPM, we applied the method to various real world datasets, such as ADLs, weblog dataset and bioinformatics dataset. At last, a SS-FPM based partial clustering method is investigated. From the non-sequential attribute site, we consider the grouping relationship that is readily available in a sequential dataset. To represent such relationship, hypergraphs are used and the focus is on how to search sub-structures in the large hypergraph. In this direction, we generalize the structure-based indexing from simple graphs to hypergraphs and propose an efficient verification method that can accelerate the sub-hypergraph matching process. Through experiments, the efficiency and effectiveness of the proposed solutions are shown.


Advances In Knowledge Discovery And Data Mining

Advances in Knowledge Discovery and Data Mining PDF
Author: Jian Pei
Publisher: Springer
ISBN: 3642374530
Size: 66.33 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 610
View: 2997

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Advances In Knowledge Discovery And Data Mining

by Jian Pei, Advances In Knowledge Discovery And Data Mining Books available in PDF, EPUB, Mobi Format. Download Advances In Knowledge Discovery And Data Mining books, The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, applications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving data mining, recommendation, multimedia data mining, stream data mining, data preprocessing and representation.


Planar Graph Drawing

Planar Graph Drawing PDF
Author: Takao Nishizeki
Publisher: World Scientific
ISBN: 9789812560339
Size: 11.57 MB
Format: PDF
Category : Computers
Languages : en
Pages : 295
View: 3319

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Planar Graph Drawing

by Takao Nishizeki, Planar Graph Drawing Books available in PDF, EPUB, Mobi Format. Download Planar Graph Drawing books, The book presents the important fundamental theorems and algorithms on planar graph drawing with easy-to-understand and constructive proofs. Extensively illustrated and with exercises included at the end of each chapter, it is suitable for use in advanced undergraduate and graduate level courses on algorithms, graph theory, graph drawing, information visualization and computational geometry. The book will also serve as a useful reference source for researchers in the field of graph drawing and software developers in information visualization, VLSI design and CAD.