Graph Partitioning and Graph Clustering

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Publisher : American Mathematical Soc.
ISBN 13 : 0821890387
Total Pages : 258 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Graph Partitioning and Graph Clustering by : David A. Bader

Download or read book Graph Partitioning and Graph Clustering written by David A. Bader and published by American Mathematical Soc.. This book was released on 2013-03-18 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph partitioning and graph clustering are ubiquitous subtasks in many applications where graphs play an important role. Generally speaking, both techniques aim at the identification of vertex subsets with many internal and few external edges. To name only a few, problems addressed by graph partitioning and graph clustering algorithms are: What are the communities within an (online) social network? How do I speed up a numerical simulation by mapping it efficiently onto a parallel computer? How must components be organized on a computer chip such that they can communicate efficiently with each other? What are the segments of a digital image? Which functions are certain genes (most likely) responsible for? The 10th DIMACS Implementation Challenge Workshop was devoted to determining realistic performance of algorithms where worst case analysis is overly pessimistic and probabilistic models are too unrealistic. Articles in the volume describe and analyze various experimental data with the goal of getting insight into realistic algorithm performance in situations where analysis fails.

Algebraic Graph Algorithms

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Publisher : Springer Nature
ISBN 13 : 3030878864
Total Pages : 229 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Algebraic Graph Algorithms by : K. Erciyes

Download or read book Algebraic Graph Algorithms written by K. Erciyes and published by Springer Nature. This book was released on 2021-11-17 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and/or graph algorithms.

Managing and Mining Graph Data

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Publisher : Springer Science & Business Media
ISBN 13 : 1441960457
Total Pages : 623 pages
Book Rating : 4.4/5 (419 download)

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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.

Knowledge Discovery in Databases: PKDD 2004

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Publisher : Springer Science & Business Media
ISBN 13 : 3540231080
Total Pages : 578 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Knowledge Discovery in Databases: PKDD 2004 by : Jean-Francois Boulicaut

Download or read book Knowledge Discovery in Databases: PKDD 2004 written by Jean-Francois Boulicaut and published by Springer Science & Business Media. This book was released on 2004-09-10 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2004, held in Pisa, Italy, in September 2004 jointly with ECML 2004. The 39 revised full papers and 9 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 194 papers submitted to PKDD and 107 papers submitted to both, PKDD and ECML. The papers present a wealth of new results in knowledge discovery in databases and address all current issues in the area.

Finding Out About

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Publisher : Cambridge University Press
ISBN 13 : 9780521630283
Total Pages : 388 pages
Book Rating : 4.6/5 (32 download)

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Book Synopsis Finding Out About by : Richard K. Belew

Download or read book Finding Out About written by Richard K. Belew and published by Cambridge University Press. This book was released on 2000 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains how to build useful tools for searching collections of text and other media.

Encyclopedia of Machine Learning

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Publisher : Springer Science & Business Media
ISBN 13 : 0387307680
Total Pages : 1061 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis Encyclopedia of Machine Learning by : Claude Sammut

Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Graph Partitioning

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Publisher : John Wiley & Sons
ISBN 13 : 1118601254
Total Pages : 301 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Graph Partitioning by : Charles-Edmond Bichot

Download or read book Graph Partitioning written by Charles-Edmond Bichot and published by John Wiley & Sons. This book was released on 2013-01-24 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph partitioning is a theoretical subject with applications in many areas, principally: numerical analysis, programs mapping onto parallel architectures, image segmentation, VLSI design. During the last 40 years, the literature has strongly increased and big improvements have been made. This book brings together the knowledge accumulated during many years to extract both theoretical foundations of graph partitioning and its main applications.

Graph-Based Clustering and Data Visualization Algorithms

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Publisher : Springer Science & Business Media
ISBN 13 : 1447151585
Total Pages : 120 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Graph-Based Clustering and Data Visualization Algorithms by : Ágnes Vathy-Fogarassy

Download or read book Graph-Based Clustering and Data Visualization Algorithms written by Ágnes Vathy-Fogarassy and published by Springer Science & Business Media. This book was released on 2013-05-24 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

Algorithm Engineering

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Publisher : Springer
ISBN 13 : 3319494872
Total Pages : 428 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Algorithm Engineering by : Lasse Kliemann

Download or read book Algorithm Engineering written by Lasse Kliemann and published by Springer. This book was released on 2016-11-10 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithm Engineering is a methodology for algorithmic research that combines theory with implementation and experimentation in order to obtain better algorithms with high practical impact. Traditionally, the study of algorithms was dominated by mathematical (worst-case) analysis. In Algorithm Engineering, algorithms are also implemented and experiments conducted in a systematic way, sometimes resembling the experimentation processes known from fields such as biology, chemistry, or physics. This helps in counteracting an otherwise growing gap between theory and practice.

Spectral Graph Theory

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Publisher : American Mathematical Soc.
ISBN 13 : 0821803158
Total Pages : 228 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Spectral Graph Theory by : Fan R. K. Chung

Download or read book Spectral Graph Theory written by Fan R. K. Chung and published by American Mathematical Soc.. This book was released on 1997 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text discusses spectral graph theory.

Bipartite Graph Partitioning and Data Clustering

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Publisher :
ISBN 13 :
Total Pages : 8 pages
Book Rating : 4.:/5 (684 download)

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Book Synopsis Bipartite Graph Partitioning and Data Clustering by :

Download or read book Bipartite Graph Partitioning and Data Clustering written by and published by . This book was released on 2001 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, the authors propose a new data clustering method based on partitioning the underlying biopartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. They show that an approximate solution to the minimization problem can be obtained by computing a partial singular value decomposition (SVD) of the associated edge weight matrix of the bipartite graph. They point out the connection of their clustering algorithm to correspondence analysis used in multivariate analysis. They also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, they apply their clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency.

Data Mining for Scientific and Engineering Applications

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Publisher : Springer Science & Business Media
ISBN 13 : 9781402001147
Total Pages : 632 pages
Book Rating : 4.0/5 (11 download)

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Book Synopsis Data Mining for Scientific and Engineering Applications by : R.L. Grossman

Download or read book Data Mining for Scientific and Engineering Applications written by R.L. Grossman and published by Springer Science & Business Media. This book was released on 2001-10-31 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Graph Mining

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Publisher : Morgan & Claypool Publishers
ISBN 13 : 160845116X
Total Pages : 209 pages
Book Rating : 4.6/5 (84 download)

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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

Graph Representation Learning

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Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Data Clustering

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Publisher : CRC Press
ISBN 13 : 1315362783
Total Pages : 652 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Data Clustering by : Charu C. Aggarwal

Download or read book Data Clustering written by Charu C. Aggarwal and published by CRC Press. This book was released on 2018-09-03 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction

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Author :
Publisher :
ISBN 13 :
Total Pages : 8 pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction by : Shubhendu Trivedi

Download or read book Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction written by Shubhendu Trivedi and published by . This book was released on 2012 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of the members of the given cluster than a predictor trained on the entire data-set. Previous work has used this basic premise to construct a simple yet strong bagging strategy. However, such models have one significant drawback: Instances (such as students) are clustered while features (tutor usage features/items) are left alone. One-way clustering by using some objective function measures the degree of homogeneity between data instances. Often it is noticed that features also influence final prediction in homogeneous groups. This indicates a duality in the relationship between clusters of instances and clusters of features. Co-Clustering simultaneously measures the degree of homogeneity in both data instances and features, thus also achieving clustering and dimensionality reduction simultaneously. Students and features could be modelled as a bipartite graph and a simultaneous clustering could be posed as a bipartite graph partitioning problem. In this paper we integrate an effective bagging strategy with Co-Clustering and present results for prediction of out-of-tutor performance of students. We report that such a strategy is very useful and intuitive, even improving upon performance achieved by previous work. (Contains 4 figures and 2 tables.) [Additional funding for this research was provided by the United States Army. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.].

Machine Learning and Knowledge Discovery in Databases

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Publisher :
ISBN 13 : 9783642158841
Total Pages : 0 pages
Book Rating : 4.1/5 (588 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Jos L. Balc Zar

Download or read book Machine Learning and Knowledge Discovery in Databases written by Jos L. Balc Zar and published by . This book was released on 2011-03-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: