Download or read book Scalable Algorithms for Data and Network Analysis written by Shang-Hua Teng and published by . This book was released on 2016 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of Big Data, efficient algorithms are now in higher demand more than ever before. While Big Data takes us into the asymptotic world envisioned by our pioneers, it also challenges the classical notion of efficient algorithms: Algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. In this tutorial, I will survey a family of algorithmic techniques for the design of provably-good scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning. They also include spectral graph-theoretical methods, such as those used for computing electrical flows and sampling from Gaussian Markov random fields. These methods exemplify the fusion of combinatorial, numerical, and statistical thinking in network analysis. I will illustrate the use of these techniques by a few basic problems that are fundamental in network analysis, particularly for the identification of significant nodes and coherent clusters/communities in social and information networks. I also take this opportunity to discuss some frameworks beyond graph-theoretical models for studying conceptual questions to understand multifaceted network data that arise in social influence, network dynamics, and Internet economics.
Download or read book Scalable Algorithms for Data and Network Analysis written by Shang-Hua Teng and published by . This book was released on 2016-05-04 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of Big Data, efficient algorithms are in high demand. It is also essential that efficient algorithms should be scalable. This book surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning.
Download or read book Scalable Algorithms for the Analysis of Massive Networks written by Eugenio Angriman and published by . This book was released on 2021* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Algorithms written by Mahmoud Parsian and published by "O'Reilly Media, Inc.". This book was released on 2015-07-13 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)
Download or read book Computing and Combinatorics written by Yixin Cao and published by Springer. This book was released on 2017-07-25 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 23rd International Conference on Computing and Combinatorics, COCOON 2017, held in Hiong Kong, China, in August 2017. The 56 full papers papers presented in this book were carefully reviewed and selected from 119 submissions. The papers cover various topics, including algorithms and data structures, complexity theory and computability, algorithmic game theory, computational learning theory, cryptography, computationalbiology, computational geometry and number theory, graph theory, and parallel and distributed computing.
Download or read book Algorithms for Big Data written by Hannah Bast and published by Springer Nature. This book was released on 2022 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book surveys the progress in addressing selected challenges related to the growth of big data in combination with increasingly complicated hardware. It emerged from a research program established by the German Research Foundation (DFG) as priority program SPP 1736 on Algorithmics for Big Data where researchers from theoretical computer science worked together with application experts in order to tackle problems in domains such as networking, genomics research, and information retrieval. Such domains are unthinkable without substantial hardware and software support, and these systems acquire, process, exchange, and store data at an exponential rate. The chapters of this volume summarize the results of projects realized within the program and survey-related work. This is an open access book.
Download or read book Scalable Fuzzy Algorithms for Data Management and Analysis Methods and Design written by Laurent, Anne and published by IGI Global. This book was released on 2009-10-31 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents up-to-date techniques for addressing data management problems with logic and memory use"--Provided by publisher.
Download or read book Working with Network Data written by James Bagrow and published by Cambridge University Press. This book was released on 2024-05-31 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing examples from real-world networks, this essential book traces the methods behind network analysis and explains how network data is first gathered, then processed and interpreted. The text will equip you with a toolbox of diverse methods and data modelling approaches, allowing you to quickly start making your own calculations on a huge variety of networked systems. This book sets you up to succeed, addressing the questions of what you need to know and what to do with it, when beginning to work with network data. The hands-on approach adopted throughout means that beginners quickly become capable practitioners, guided by a wealth of interesting examples that demonstrate key concepts. Exercises using real-world data extend and deepen your understanding, and develop effective working patterns in network calculations and analysis. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.
Download or read book On the Analysis of Complex Networks written by Feizi-Khankandi Feizi and published by . This book was released on 2016 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Network models provide a unifying framework for understanding dependencies among variables in data-driven and engineering sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. In this thesis, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several network science problems. In Chapters 2-4, we consider network inference challenges. In Chapter 2, we introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association suitable for evaluation on large datasets. We characterize a solution of the NMC optimization using geometric properties of Hilbert spaces for finite discrete and jointly Gaussian random variables. We illustrate an application of NMC and multiple MC in inference of graphical models for bijective, possibly non-monotone, functions of jointly Gaussian variables. As a demonstration of NMC's utility, we infer nonlinear gene association networks and modules in cancer datasets and validate them using survival times of patients. In Chapter 3, we develop a network integration framework to infer gene regulatory networks in human and model organisms fly and worm using diverse and high-throughput datasets. Inferred regulatory interactions have significant overlap with known edges, indicating the robustness and accuracy of the proposed network inference framework. In Chapter 4, we formulate the transitive noise problem in networks as the inverse of matrix transitive closure and introduce an algorithm to solve it efficiently. We demonstrate the effectiveness of our approach in several applications such as regulatory network inference, protein contact map inference and strong collaboration tie inference. In Chapters 5-8, we consider network analysis challenges. In Chapter 5, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. This problem is essential in comparative analysis across large datasets and networks. To solve this combinatorial problem, we present a new scalable spectral algorithm which creates an eigenvector relaxation for the underlying optimization. We prove the optimality of the method under certain technical conditions, and show its effectiveness over various synthetic networks as well as in comparative analysis of gene regulatory networks across human, fly and worm species. In Chapter 6, we consider the source inference problem where the goal is to identify the source(s) of propagated signals across biological, social and engineered networks. To solve this problem, we propose a computationally tractable general method based on a path-based network diffusion kernel. We prove mean-field optimality of this method for different scenarios and show its effectiveness over several synthetic networks as well as in identifying sources in a Digg social news network. In Chapter 7, we consider the problem of learning low dimensional structures (such as clusters) in large networks. Here we introduce logistic Random Dot Product Graphs (RDPGs) as a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a scalable spectral method that solves the maximum likelihood inference problem asymptotically exactly. This leads to a new scalable spectral network clustering algorithm that is robust under different clustering setups. In Chapter 8, we consider the biclustering problem, the analog of clustering on bipartite graphs. This problem has several applications such as inference of co-regulated genes, document classification, and so on. Here we propose an algorithm based on message-passing that closely approximates a general likelihood function and excels at resolving the overlaps between biclusters. In Chapters 9-12, we consider design challenges of systems and algorithms for engineering networks such as communication networks. In Chapters 9-10, we create a connection between compressive sensing and traditional information theoretic techniques in source, channel and network coding and propose a joint coding scheme over wireless networks based on random projection and restricted eigenvalue principles. Moreover, we characterize fundamental results on the trade-off between the communication rate and the decoding complexity. In Chapters 11-12, we propose an adaptive nonuniform sampling framework, in which time increments between samples are determined as a function of the most recent increments and sample values, obviating the need to track time stamps. We analyze the performance of the proposed method for different stochastic and deterministic signal models and show its effectiveness to enhance measurements of heart ECG signals.
Download or read book Network Algorithms Data Mining and Applications written by Ilya Bychkov and published by Springer Nature. This book was released on 2020-02-22 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis.
Download or read book Algorithms for Data Science written by Brian Steele and published by Springer. This book was released on 2016-12-25 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Download or read book Big Data Analysis New Algorithms for a New Society written by Nathalie Japkowicz and published by Springer. This book was released on 2015-12-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.
Download or read book Scalable Algorithms written by Vassil Alexandrov and published by CRC Press. This book was released on 2016-10-15 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novel scalable scientific algorithms are needed to enable key science applications and to exploit the computational power of largescale systems. This is especially true for the current tier of leading petascale machines and the road to exascale computing as HPC systems continue to scale up in compute node and processor core count. These extreme-scale systems require novel scientific algorithms to hide network and memory latency, have very high computation/communication overlap, have minimal communication, and no synchronization points. Authored by two of the leading experts in this area, this book focuses on the latest advances in scalable algorithms for large scale systems.
Download or read book Scalabale Algorithms for Updating Large Scale Dynamic Networks written by Sriram Srinivasan and published by . This book was released on 2020 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth of social media and data in various domains increased the interest in analyzing network algorithms. The networks are highly unstructured and exhibit poor locality, which has been a challenge for developing scalable parallel algorithms. The state-of-the-art network algorithms such as Prim's algorithm for Minimum Spanning Tree, Dijkstra's algorithm for Single Source Shortest Path, Google's Page Rank algorithm, and iSpan algorithm for detecting strongly connected components are designed and optimized for static networks. For the networks that change with time, i.e. the dynamic networks (such as social networks, biological networks, or temporal networks) the above-mentioned approaches can only be utilized if they are computed from scratch each time. Performing a computation from scratch for a significant amount of changes is not only computationally expensive, however, increases the memory footprint and the execution time. In the case of dynamic networks, developing scalable parallel algorithms is very challenging and there has been a very limited amount of research work that has been performed when compared to developing parallel scalable algorithms for static networks. To address the above challenges, this Ph. D. dissertation proposes a new high performance, scalable, portable, open-source software package, and an efficient network data structure to update the dynamic networks on the fly. This approach is different from the naive approach which is the re-computation from scratch and is scalable for random, small-world, scale-free, real-world, and synthetic networks. The software package currently is implemented on a shared memory system and GPU which updates network properties such as Connected Components (CC), Minimum Spanning Tree (MST), Single Source Shortest Path (SSSP), Page Rank (PR), and Strongly Connected Components(SCC). The key attributes of the software are faster insertions and deletions. Additionally, the software takes less time and memory for updating the networks when compared to the state of the art Galois(CPU), and Gunrock (GPU). The GPU implementation processes over 50 million updates for updating SSSP on a real-world network in under 300 seconds. This dissertation also provides a novel shared memory implementation of detecting, overlapping, and non-overlapping communities on static networks using Permanence. Detecting communities on large scale networks is a fundamental operation in various domains. Detecting correct communities is a challenging problem due to the limitations of the metric such as the state-of-the-art metric modularity since it suffers from the resolution limit. This dissertation is the first attempt to implement shared memory overlapping and non-overlapping communities using permanence. The key attributes of this implementation are the accuracy of the communities when compared to the ground truth and achieve speed up to 10× when compared to its sequential implementation. The dissertation concludes with a summarization of the contributions and their improvement in large-scale network analytics and a discussion about future work in this field.
Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer. This book was released on 2013-06-04 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised full papers presented together with one invited paper were carefully reviewed and selected from 274 submissions. The 57 papers included in the first volume are organized in the following topical sections: neural networks and their applications; fuzzy systems and their applications; pattern classification; and computer vision, image and speech analysis.
Download or read book Scalable Algorithms for Contact Problems written by Zdeněk Dostál and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive treatment of recently developed scalable algorithms for solving multibody contact problems of linear elasticity. The brand-new feature of these algorithms is their theoretically supported numerical scalability (i.e., asymptotically linear complexity) and parallel scalability demonstrated in solving problems discretized by billions of degrees of freedom. The theory covers solving multibody frictionless contact problems, contact problems with possibly orthotropic Tresca's friction, and transient contact problems. In addition, it also covers BEM discretization, treating jumping coefficients, floating bodies, mortar non-penetration conditions, etc. This second edition includes updated content, including a new chapter on hybrid domain decomposition methods for huge contact problems. Furthermore, new sections describe the latest algorithm improvements, e.g., the fast reconstruction of displacements, the adaptive reorthogonalization of dual constraints, and an updated chapter on parallel implementation. Several chapters are extended to give an independent exposition of classical bounds on the spectrum of mass and dual stiffness matrices, a benchmark for Coulomb orthotropic friction, details of discretization, etc. The exposition is divided into four parts, the first of which reviews auxiliary linear algebra, optimization, and analysis. The most important algorithms and optimality results are presented in the third chapter. The presentation includes continuous formulation, discretization, domain decomposition, optimality results, and numerical experiments. The final part contains extensions to contact shape optimization, plasticity, and HPC implementation. Graduate students and researchers in mechanical engineering, computational engineering, and applied mathematics will find this book of great value and interest.
