Download or read book Fast Similarity Graph Construction Via Data Sketching Techniques written by Marefat. Hoorieh and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs are mathematical structures used to model objects and their pairwise relationships. Due to their simple but expressive abstract representation, they are commonly used to model various types of relations and processes in technological, social or biological systems and have found numerous applications. A special type of graph is the similarity graph in which nodes represent entities and there is an edge connecting two nodes if the two entities are similar based on some similarity measure. In a typical scenario, raw data of entities are provided in the form of a relational dataset, matrix or a tensor and a similarity graph is built to facilitate graph-based analysis like node importance, node classification, link prediction, community detection, outlier detection, and more. The ability to construct similarity graphs fast is important and with a potential for high impact, thus several approximation techniques have been proposed. In this work, we propose data sketching based methods for fast approximate similarity graph construction. Data sketching techniques are applied on the raw data and are designed to achieve desired error guarantees. They can drastically reduce the size of raw data on which we operate, allowing for faster construction and analysis of similarity graphs, but with approximate results. This is a desirable tradeoff for many applications in diverse domains. Through a thorough experimental evaluation, we demonstrate that our sketching methods outperform sensible baselines and competitor methods proposed for the problem. First, they are much faster than exact methods while maintaining high accuracy in constructing the similarity graph. Furthermore, our methods demonstrate significantly higher accuracy than competitive methods on generic graph analysis tasks. We demonstrate the effectiveness of our methods on different real-world graph applications.

Download or read book Efficient Graph Construction for Similarity Search on High Dimensional Data written by Leslie Kanthan and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The K nearest neighbours graph, denoted KNNG, is an essential graph in data mining and machine learning. However, despite its vital significance, exact construction of this graph for high dimensional datasets (d 10) is inefficient (O(n2) computational complexity). Approximate algorithms have been shown to improve upon this complexity, but compromise accuracy. In this thesis, we focus on automatically improving existing locality sensitive hashing schemes and proposing new schemes that find good trade-offs between accuracy and speed. We investigate how to obtain an LSH version with a guaranteed worst-case subquadratic cost that minimises the loss of accuracy. We implement such an algorithm and evaluate its runtime impact for different types of datasets. We implement the most popular versions and perform a detailed experimental comparison and present trends between specific LSH versions and the input dataset characteristics. Relying on the findings of this analysis, we propose Variable Radius LSH (VRLSH), a new LSH scheme that is suitable for distributed computation and capable of handling large datasets. We show how VRLSH can scale efficiently with the size of the dataset, and how it can improve the accuracy of the generated KNNG. Next, we propose three new LSH schemes that rely on the strategy of imitating biological systems. In particular, we propose RFLY, PFLY and DPFLY three schemes inspired by FLY-LSH, a recent variation of the LSH algorithm that relies on the olfactory circuit of flies, used to identify similar odours. We first experiment and expand FLY-LSH by running it on a larger number of datasets. The three proposed algorithms improve both the accuracy and the applicability of FLY-LSH on real datasets. Firstly, RFLY improves the accuracy of the generated graph by 10%. Then PFLY distributes data more appropriately in a pre-fixed number of buckets, while concurrently improving the accuracy of the generated graph. Thirdly, DPFLY adapts random projects to the input dataset, achieving 15% improvement. Hitherto, we propose a novel optimisation framework that uses machine learning techniques and genetic algorithms to automatically select a pareto frontier tuned version of the LSH schemes for a given specific input dataset. In our experiments, our optimisation framework improves the performance (both speed and accuracy) for every version of the LSH algorithm by 10% and 13% respectively. Last, we discuss future work and how the findings of this thesis can further help the research community.

Download or read book Combinatorial Scientific Computing written by Uwe Naumann and published by CRC Press. This book was released on 2012-01-25 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combinatorial Scientific Computing explores the latest research on creating algorithms and software tools to solve key combinatorial problems on large-scale high-performance computing architectures. It includes contributions from international researchers who are pioneers in designing software and applications for high-performance computing systems. The book offers a state-of-the-art overview of the latest research, tool development, and applications. It focuses on load balancing and parallelization on high-performance computers, large-scale optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications in large-scale social networks. The authors unify these seemingly disparate areas through a common set of abstractions and algorithms based on combinatorics, graphs, and hypergraphs. Combinatorial algorithms have long played a crucial enabling role in scientific and engineering computations and their importance continues to grow with the demands of new applications and advanced architectures. By addressing current challenges in the field, this volume sets the stage for the accelerated development and deployment of fundamental enabling technologies in high-performance scientific computing.

Download or read book Similarity Search and Applications written by Giuseppe Amato and published by Springer Nature. This book was released on 2019-09-24 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference on Similarity Search and Applications, SISAP 2019, held in Newark, NJ, USA, in October 2019. The 12 full papers presented together with 18 short and 3 doctoral symposium papers were carefully reviewed and selected from 42 submissions. The papers are organized in topical sections named: Similarity Search and Retrieval; The Curse of Dimensionality; Clustering and Outlier Detection; Subspaces and Embeddings; Applications; Doctoral Symposium Papers.

Download or read book Databases Theory and Applications written by Zi Huang and published by Springer. This book was released on 2017-09-18 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 28th Australasian Database Conference, ADC 2017, held in Brisbane, QLD, Australia, in September 2017. The 20 full papers presented together with 2 demo papers were carefully reviewed and selected from 32 submissions. The mission of ADC is to share novel research solutions to problems of today’s information society that fulfill the needs of heterogeneous applications and environments and to identify new issues and directions for future research and development work. The topics of the presented papers are related to all practical and theoretical aspects of advanced database theory and applications, as well as case studies and implementation experiences.

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.

Download or read book Proceedings of the 20th International Conference Companion on World Wide Web written by S. Sadagopan and published by . This book was released on 2011 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Process oriented Semantic Web Search written by D.T. Tran and published by IOS Press. This book was released on 2011-02-22 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is composed of two main parts. The first part is a general study of Semantic Web Search. The second part specifically focuses on the use of semantics throughout the search process, compiling a big picture of Process-oriented Semantic Web Search from different pieces of work that target specific aspects of the process. In particular, this book provides a rigorous account of the concepts and technologies proposed for searching resources and semantic data on the Semantic Web. To collate the various approaches and to better understand what the notion of Semantic Web Search entails, this book presents a general Semantic Web Search model. With respect to this model, the book provides a comprehensive discussion of the state-of-the-art. It elaborates on approaches for crawling, managing and searching Semantic Web resources as well as the various schemes proposed for ranking search results. Besides these specific approaches, search is also studied in a general multi-data-source scenario. This shall demonstrate how this work on search is extended and applied to the Web setting. A major feature of the book is that it considers search and the use of semantics for search also from a process point of view. Extending the general model, the book introduces the notion of Process-oriented Semantic Web Search, where semantics is exploited throughout the entire search process – from query construction to query processing up to result presentation and query refinement. Specific pieces of work targeting these individual steps of the process are combined to form a coherent and consistent picture of Process-oriented Semantic Web Search. In order to convey this general notion as well as the specific concepts and technologies developed for supporting the search process, this book presents a compilation of work called SemSearchPro and provides detailed descriptions on the underlying approaches.

Download or read book Euro Par 2018 Parallel Processing written by Marco Aldinucci and published by Springer. This book was released on 2018-08-20 with total page 829 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 24th International Conference on Parallel and Distributed Computing, Euro-Par 2018, held in Turin, Italy, in August 2018. The 57 full papers presented in this volume were carefully reviewed and selected from 194 submissions. They were organized in topical sections named: support tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; high performance architecutres and compilers; parallel and distributed data management and analytics; cluster and cloud computing; distributed systems and algorithms; parallel and distributed programming, interfaces, and languages; multicore and manycore methods and tools; theory and algorithms for parallel computation and networking; parallel numerical methods and applications; and accelerator computing for advanced applications.

Download or read book Imaging Vision and Learning Based on Optimization and PDEs written by Xue-Cheng Tai and published by Springer. This book was released on 2018-11-19 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs). It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.

Download or read book Similarity Search and Applications written by Oscar Pedreira and published by Springer Nature. This book was released on 2023-10-26 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9–11, 2023. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.

Download or read book Algorithms and Theory of Computation Handbook Volume 1 written by Mikhail J. Atallah and published by CRC Press. This book was released on 2009-11-20 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms and Theory of Computation Handbook, Second Edition: General Concepts and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many

Download or read book Robust Representation for Data Analytics written by Sheng Li and published by Springer. This book was released on 2017-08-09 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

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.

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

Download or read book Graph Drawing written by Michael Kaufmann and published by Springer Science & Business Media. This book was released on 2007-02-07 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 14th International Symposium on Graph Drawing, GD 2006, held in Karlsruhe, Germany in September 2006. The 33 revised full papers and 5 revised short papers presented together with 2 invited talks, 1 system demo, 2 poster papers and a report on the graph drawing contest were carefully selected during two rounds of reviewing and improvement from 91 submissions. All current aspects in graph drawing are addressed ranging from foundational and methodological issues to applications for various classes of graphs in a variety of fie.

Download or read book Similarity Search and Applications written by Christian Beecks and published by Springer. This book was released on 2017-09-25 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Similarity Search and Applications, SISAP 2017, held in Munich, Germany, in October 2017. The 23 full papers presented were carefully reviewed and selected from 53 submissions. The papers deal with issues surrounding the theory, design, analysis, practice, and application of content-based and feature-based similarity search. They are organized in the following topical sections: approximate similarity search; improving similarity search methods and applications; distances for complex objects; outlier detection; indexing and applications; and applications and specific domains. The paper 'A New Perspective on the Tree Edit Distance' is published open access under a CC BY 4.0 license at link.springer.com.